An introduction to meta analysis

Abstract

Meta analysis is a statistical technique for synthesizing of results obtained from multiple studies. It is the process of combining, summarizing, and reanalyzing previous quantitative research. It yields a quantitative summary of the pooled results. Decisions of the validity of a hypothesis cannot be based on the results of a single study, because results typically vary from one study to the next. Traditional methods do not allow involving more than a few studies. Meta analysis provides certain procedures to synthesize data across studies. When the treatment effect (or effect size) is consistent from one study to the next, meta-analysis can be used to identify this common effect. When the effect varies from one study to the next, meta-analysis may be used to identify the reason for the variation. The amount of accumulated information in fast developing fields of science such as biology, medicine, education, pharmacology, physics, etc. increased very quickly after the Second World War. This lead to large amounts of literature which was not systematized. One problem in education might include ten independent studies. All of the studies might be performed by different researchers, using different techniques, and different measurements. The idea of integrating the research literature was proposed by Glass (1976, 1977). He referred it as the meta analysis of research. There are three major meta analysis approaches: combining significance levels, combining estimates of effect size for fixed effect size models and random effect size models, and vote-counting method.