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A meta-analysis is a statistical analysis that combines the results of multiple scientific studies. Meta-analyses can be performed when there are multiple scientific studies addressing the same question, with each individual study reporting measurements that are expected to have some degree of error. The aim then is to use approaches from statistics to derive a pooled estimate closest to the unknown common truth based on how this error is perceived. It is thus a basic methodology of Metascience. Meta-analytic results are considered the most trustworthy source of evidence by the evidence-based medicine literature.
In statistical hypothesis testing, a result has statistical significance when a result at least as "extreme" would be very infrequent if the null hypothesis were true. More precisely, a study's defined significance level, denoted by , is the probability of the study rejecting the null hypothesis, given that the null hypothesis is true; and the p-value of a result, , is the probability of obtaining a result at least as extreme, given that the null hypothesis is true. The result is statistically significant, by the standards of the study, when . The significance level for a study is chosen before data collection, and is typically set to 5% or much lower—depending on the field of study.
Social statistics is the use of statistical measurement systems to study human behavior in a social environment. This can be accomplished through polling a group of people, evaluating a subset of data obtained about a group of people, or by observation and statistical analysis of a set of data that relates to people and their behaviors.
In statistics, an effect size is a value measuring the strength of the relationship between two variables in a population, or a sample-based estimate of that quantity. It can refer to the value of a statistic calculated from a sample of data, the value of a parameter for a hypothetical population, or to the equation that operationalizes how statistics or parameters lead to the effect size value. Examples of effect sizes include the correlation between two variables, the regression coefficient in a regression, the mean difference, or the risk of a particular event happening. Effect sizes complement statistical hypothesis testing, and play an important role in power analyses, sample size planning, and in meta-analyses. The cluster of data-analysis methods concerning effect sizes is referred to as estimation statistics.
Gene V Glass is an American statistician and researcher working in educational psychology and the social sciences. According to the science writer Morton Hunt, he coined the term "meta-analysis" and illustrated its first use in his presidential address to the American Educational Research Association in San Francisco in April, 1976. The most extensive illustration of the technique was to the literature on psychotherapy outcome studies, published in 1980 by Johns Hopkins University Press under the title Benefits of Psychotherapy by Mary Lee Smith, Gene V Glass, and Thomas I. Miller. Gene V Glass is a Regents' Professor Emeritus at Arizona State University in both the educational leadership and policy studies and psychology in education divisions, having retired in 2010 from the Mary Lou Fulton Institute and Graduate School of Education. Currently he is a senior researcher at the National Education Policy Center, a Research Professor in the School of Education at the University of Colorado Boulder, and a Lecturer in the Connie L. Lurie College of Education at San Jose State University. In 2003, he was elected to membership in the National Academy of Education.
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In statistics, (between-) study heterogeneity is a phenomenon that commonly occurs when attempting to undertake a meta-analysis. In a simplistic scenario, studies whose results are to be combined in the meta-analysis would all be undertaken in the same way and to the same experimental protocols. Differences between outcomes would only be due to measurement error. Study heterogeneity denotes the variability in outcomes that goes beyond what would be expected due to measurement error alone.
Geoffrey D. Borman is an American quantitative methodologist and policy analyst. He received his Ph.D. from the University of Chicago in 1997 and is currently the Alice Wiley Snell Endowed Professor at Arizona State University, Director of the Arizona State University Education Sciences Graduate Program, and Editor of Educational Evaluation and Policy Analysis.
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Eric Alan Hanushek is an economist who has written prolifically on public policy with a special emphasis on the economics of education. Since 2000, he has been a Paul and Jean Hanna Senior Fellow at the Hoover Institution, an American public policy think tank located at Stanford University in California. He was awarded the Yidan Prize for Education Research in 2021.
Harry V. Roberts (1923–2004), American statistician, was a distinguished teacher and a pioneer in looking at the applications of Bayesian statistics to business decision making and in Total Quality Management.
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Ingram Olkin was a professor emeritus and chair of statistics and education at Stanford University and the Stanford Graduate School of Education. He is known for developing statistical analysis for evaluating policies, particularly in education, and for his contributions to meta-analysis, statistics education, multivariate analysis, and majorization theory.
Larry Alan Wasserman is a Canadian-American statistician and a professor in the Department of Statistics & Data Science and the Machine Learning Department at Carnegie Mellon University.
Estimation statistics, or simply estimation, is a data analysis framework that uses a combination of effect sizes, confidence intervals, precision planning, and meta-analysis to plan experiments, analyze data and interpret results. It complements hypothesis testing approaches such as null hypothesis significance testing (NHST), by going beyond the question is an effect present or not, and provides information about how large an effect is. Estimation statistics is sometimes referred to as the new statistics.
The Yidan Prize (/i:dan/) is a prize founded in 2016 by Chen Yidan for "contributions to education research and development". The prize is financed and governed by a HK$2.5 billion independent trust. It is a global, inclusive education award which recognizes changemakers who inspire progress in education for a better world, and has been referred to as the largest education prize on earth.
Betsy Jane Becker is an American researcher on meta-analysis and educational psychometrics. She is the Mode L. Stone Distinguished Professor of Educational Statistics in the Department of Statistics at Florida State University, and previously served as the Chair of the Department of Educational Psychology and Learning Systems at Florida State. Becker is a co-author of a heavily cited consensus report on standards for meta-analysis in epidemiology.
Jessica Gurevitch is a plant ecologist known for meta-analysis in the fields of ecology and evolution.
References
- ↑ "Larry V. Hedges". Northwestern University . Retrieved May 6, 2013.
- ↑ "Department of Statistics: Larry Hedges". Northwestern University. May 19, 2009. Retrieved May 16, 2010.
- ↑ Sweet, Lynn (October 19, 2011), "Obama taps Northwestern U. prof Larry Hedges for education panel", Chicago Sun-Times .
- ↑ "The official website of the Yidan Prize and Yidan Prize Foundation". Yidan Prize Foundation. Retrieved May 12, 2023.
