In the field of education, there is seemingly endless data attempting to give the final word on whether more spending per pupil means higher student achievement. Sifting through it to come up with one common finding is an overwhelming task.
That's why Terri Pigott, PhD, associate professor of research methodology in the School of Education, and Meng-Jia Wu, PhD, assistant professor of research methodology, are using a three-year grant from the National Science Foundation to develop new methods for metaanalysis, a statistical technique that combines the results of studies on the same topic.
Sound confusing? It's a common approach in medicine. For example, researchers often test several small groups of patients when looking at drug effectiveness. When they try to consolidate the findings, it sometimes looks as if the results disagree with each other. Meta-analysis helps determine if there are any common learnings. "Meta-analysis lays out the landscape and shows us what has been done, which findings we can feel comfortable with, and what questions still need to be answered," says Pigott.
Meta-analysis does not yet have the proper techniques for dealing with complicated studies using different controls and variables, such as per-pupil spending and student-achievement research. So Pigott and Wu are focusing their work on the development of new analytical techniques for accumulating large bodies of research. Their findings could help educators and lawmakers get to the bottom of the debate over school spending and student achievement and, ultimately, affect public policy. If successful, the new techniques may well have applications not only in the field of education, but across many disciplines and areas of study.
Jenny Kustra-Quinn