"Policy discussions today routinely demand that proposals be evidence-based -- without really understanding that the reliability and validity of what passes as evidence varies widely. Murnane and Willett have done a remarkable job of helping both producers and consumers to understand what is good evidence and how it can be produced. Methods Matter explains lucidly how the causal impact of educational and social interventions can be estimated from quantitative data, using a panoply of innovative empirical approaches." --Eric A. Hanushek, Senior Fellow, Hoover Institution, Stanford University
is about research designs and statistical analyses for drawing valid and reliable causal inferences from data about real-world problems. The book's most telling feature is the wide range of education research examples that it uses to illustrate each point made. By presenting powerful research methods in the context of important research questions the authors are able to draw readers quickly and deeply into the material covered. New and experienced researchers from many fields will learn a lot from reading Methods Matter
and will enjoy doing so."--Howard S. Bloom, Chief Social Scientist, MDRC
"Richard J. Murnane and John B. Willett provide a broadly accessible account of causal inference in educational research. They consider basic principles- how to define causal effects, frame causal questions, and design experiments- while also gently introducing important topics that have previously been obscure to non-specialists: randomization by group, natural experiments, instrumental variables, regression discontinuity, and propensity scores. Using a wide range or examples, the authors teach their readers to identify and challenge key assumptions underlying claims about what works in education. This book will improve educational research by challenging researchers and policy-makers to think more rigorously about the evidence and assumptions underlying their work." -- Stephen W. Raudenbush, Lewis Sebring Distinguished Service Professor, Department of Sociology, University of Chicago
"I strongly recommend Methods Matter to anyone who intends to conduct research on the causal impact of education programs and policies. Henceforth, a graduate course in education research methods that doesn't rely on it should be considered suspect. Methods Matter should also be essential reading for those who want to be critical consumers of advanced education research. Methods Matter
very much, and so does this book. It is a very good book that signals a coming of age of the field."
--Grover Whitehurst, Director, Brown Center on Education Policy, Brookings Institute
"To be useful for development policy, educational research has to shed more light on how resources for education can produce more learning, more knowledge, more skills. In this book, Professors Richard Murnane and John Willett discuss a range of empirical methods for estimating causal relationships and review their applications in educational research. They translate complex statistical concepts into clear, accessible language and provide the kind of analytical guidance that a graduate student or young researcher might obtain only after years of experience with these methods. This volume is a very readable companion to any statistics textbook or statistical program on evaluation methods."
--Elizabeth M. King, Director, Education, The World Bank
About the Author
Richard J. Murnane, Juliana W. and William Foss Thompson Professor of Education and Society at Harvard University, is an economist who focuses his research on the relationships between education and the economy, teacher labor markets, the determinants of children's achievement, and strategies for making schools more effective.
John B. Willett, Charles William Eliot Professor of Education at Harvard University, is a quantitative methodologist who has devoted his career to improving the research design and data-analytic methods used in education and the social sciences, with a particular emphasis on the design of longitudinal research and the analysis of longitudinal data .