Why are effect sizes used when conducting meta-analysis?

Meta-analysis is the process of quantitatively synthesizing results from numerous experimental studies. One of the main goals for conducting a meta-analysis is to estimate an overall or combined effect of an intervention/s across multiple studies. For example, Visible Learning (2009) conducted a synthesis of meta-analyses of research from various educational contexts measuring a large number of educational interventions to quantify each contributor's effect on the outcome of student learning and achievement. The broader the pool of research data that is included, the more accurate the quantitative estimate can be on how much particular contributors (e.g., teacher feedback) affect student achievement learning and achievement over others (e.g., homework). Using effect sizes is one of the most common ways of robustly assessing the effects of interventions across studies. Further, effect sizes promote scientific inquiry because when a particular experimental study has been replicated, the different effect size estimates from those studies can be easily combined to produce an overall best estimate of the intervention effect size. The basis of the method is straightforward and much of the usefulness of meta-analysis is its simplicity.