| Larry V. Hedges | |
| Birth Name: | Larry Vernon Hedges |
| Nationality: | American |
| Fields: | Statistics |
| Workplaces: | University of Chicago Northwestern University |
| Alma Mater: | Stanford University (Ph.D., 1980) |
| Thesis Title: | Combining the Results of Experiments Using Different Scales of Measurement |
| Thesis Url: | https://www.researchgate.net/publication/35762802_Combining_the_results_of_experiments_using_different_scales_of_measurement |
| Thesis Year: | 1980 |
| Doctoral Advisor: | Ingram Olkin |
| Known For: | Meta-analysis Statistical methodology |
| Awards: | Yidan Prize for Education Research (2018) |
| Spouses: | )--> |
| Partners: | )--> |
Larry Vernon Hedges is a researcher in statistical methods for meta-analysis and evaluation of education policy. He is Professor of Statistics and Education and Social Policy, Institute for Policy Research, Northwestern University. Previously, he was the Stella M. Rowley Distinguished Service Professor of Education, Sociology, Psychology, and Public Policy Studies at the University of Chicago.[1] [2] He is a member of the National Academy of Education and a fellow of the American Academy of Arts and Sciences, the American Educational Research Association, the American Psychological Association, and the American Statistical Association.[3] In 2018, he received the Yidan Prize for Education Research, the world's most prestigious and largest education prize, i.e. USD four million.[4]
He has authored a number of articles and books on statistical methods for meta-analysis, which is the use of statistical methods for combining results from different studies. He also suggested several estimators for effect sizes and derived their properties. He carried out research on the relation of resources available to schools and student achievement, most notably the relation between class size and achievement.
In 1981, Hedges published a paper describing the unbiased standardized mean difference, the g statistic.[5] "It turns out that [Cohen's] d has a slight bias, tending to overestimate the absolute value of in small samples. This bias can be removed by a simple correction that yields an unbiased estimate of, with the unbiased estimate sometimes called Hedges’ g."[6]