Methods Matter

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 .

• 1 The Challenge for Educational Research1.1 The Long Quest1.2 The Quest is World-Wide1.3 What this Book is About1.4 What to Read Next 2 The Importance of Theory2.1 What is Theory?2.2 Theory in Education2.3 Voucher Theory2.4 What Kind of Theories?2.5 What to Read Next 3 Designing Research to Address Causal Questions3.1 Conditions to Strive for in All Research3.2 Making Causal Inferences3.3 Past Approaches To Answering Causal Questions in Education3.4 The Key Challenge of Causal Research3.5 What to Read Next 4 Investigator-Designed Randomized Experiments4.1 Conducting Randomized Experiments4.1.1 An Example of a "Two-Group" Experiment4.2 Analyzing Data from Randomized Experiments4.2.1 Better Your Research Design, the Simpler Your Data-Analysis4.2.2 Bias and Precision in the Estimation of Experimental Effects4.3 What to Read Next 5 Challenges in Designing, Implementing, and Learning from Randomized Experiments5.1 Critical Decisions in the Design of Experiments 5.1.1 Defining the Treatment Being Evaluated 5.1.2 Defining the Population from Which Participants Will Be Sampled 5.1.3 Deciding Which Outcomes to Measure 5.1.4 Deciding How Long To Track Participants5.2 Threats to Validity of Randomized Experiments 5.2.1 Contamination of the treatment-control contrast 5.2.2 Cross-Overs 5.2.3 Attrition from the sample 5.2.4 Participation in an Experiment Itself Affects Participants' Behavior 5.3 Gaining Support for Conducting Randomized Experiments: Examples from India 5.3.1 Evaluating an Innovative Input Approach 5.3.2 Evaluating an Innovative Incentive Policy 5.4 What to Read Next6 Statistical Power and Sample Size6.1 Statistical Power 6.1.1 Reviewing the Process of Statistical Inference6.1.2 Defining Statistical Power6.2 Factors Affecting Statistical Power 6.2.1 The Strengths and Limitations of Parametric Tests6.2.2 The Benefits of Covariates6.2.3 The Reliability of the Outcome Measure Matters6.2.4 The Choice between One-Tailed and Two-Tailed Tests6.3 What to Read Next 7 Experimental Research When Participants Are Clustered within Intact Groups 7.1 Using the Random-Intercepts Multilevel Model to Estimate Effect Size When Intact Groups of Participants Were Randomized To Experimental Conditions 7.2 Statistical Power When Intact Groups of Participants Were Randomized To Experimental Conditions7.2.1. Statistical Power of the Cluster-Randomized Design and Intraclass Correlation7.3 Using Fixed-Effects Multilevel Models to Estimate Effect Size When Intact Groups of Participants are Randomized To Experimental Conditions 7.3.1 Specifying a "Fixed-Effects" Multilevel Model7.3.2. Choosing Between Random- and Fixed-Effects Specifications7.2 What to Read Next 8 Using Natural Experiments To Provide "Arguably Exogenous" Treatment Variability8.1 Natural- and Investigator-Designed Experiments: Similarities and Differences8.2 Two Examples of Natural Experiments8.2.1 The Vietnam Era Draft Lottery8.2.2 The Impact of an Offer of Financial Aid for College8.3 Sources of Natural Experiments8.4 Choosing the Width of the Analytic Window8.5 Threats to Validity in Natural Experiments with a Discontinuity Design8.5.1 Accounting for the Relationship between the Forcing Variable and the Outcome in a Discontinuity Design8.5.2 Actions by Participants Can Undermine Exogenous Assignment to Experimental Conditions in a Natural Experiment with a Discontinuity Design8.6 What to Read Next? 9 Estimating Causal Effects Using a Regression-Continuity Approach 9.1 Maimonides' Rule and the Impact of Class Size on Student Achievement9.1.1 A Simple "First Difference" Analysis9.1.2 A "Difference-in-Differences" Analysis9.1.3 A Basic "Regression-Discontinuity" Analysis 9.1.4 Choosing an Appropriate "Window" or "Bandwidth" 9.2 Generalizing the Relationship between Outcome and Forcing Variable 9.2.1 Specification Checks Using Pseudo-Outcomes and Pseudo-Cutoffs9.2.2 RD Designs and Statistical Power9.3 Additional Threats to Validity in an RD Design9.4 What to Read Next 10 Introducing Instrumental Variables Estimation10.1 Introducing Instrumental Variables Estimation10.1.1 Investigating the Relationship Between an Outcome and a Potentially- Endogenous Question Predictor Using OLS Regression Analysis10.1.2 Instrumental Variables Estimation10.2 Two Critical Assumptions That Underpin Instrumental Variables Estimation10.3 Alternative Ways of Obtaining the IV Estimate10.3.1 Obtaining an IV Estimate by the Method of Two-Stage Least-Squares 10.3.2 Obtaining an IVE by Simultaneous Equations Estimation10.4 Extensions of the Basic IVE Approach10.4.1 Incorporating Exogenous Covariates into IV Estimation10.4.2 Incorporating Multiple Instruments into the First-Stage Model10.4.3 Examining the Impact of Interactions between the Endogenous Question Predictor and Exogenous Covariates in the Second-Stage Model 10.4.4 Choosing Appropriate Functional Forms for the Outcome/Predictor Relationships in the First- and Second-Stage Models10.5 Finding and Defending Instruments10.5.1 Proximity of Educational Institutions10.5.2 Institutional Rules and Personal Characteristics10.5.3 Deviations from Cohort Trends 10.5.4 The Search Continues 10.6 What To Read Next 11 Using IVE to Recover the Treatment Effect in a Quasi-Experiment11.1 The Notion of a "Quasi-Experiment"11.2 Using IVE to Estimate the Causal Impact of a Treatment in a Quasi-Experiment11.3 Further Insight into the IVE (LATE) Estimate, in the Context of Quasi- Experimental Data11.4 Using IVE to Resolve "Fuzziness" in a Regression Discontinuity Design 11.5 What To Read Next 12 Dealing with Bias in Treatment Effects Estimated from Non-Experimental Data12.1 Reducing Observed Bias by the Method of Stratification 12.1.1 Stratifying on a Single Covariate 12.1.2 Stratifying on Covariates12.2 Reducing Observed Bias by Direct Control for Covariates Using Regression Analysis12.3 Reducing Observed Bias Using A "Propensity Score" Approach12.3.1 Estimation of the Treatment Effect by Stratifying on Propensity Scores12.3.2 Estimation of the Treatment Effect by Matching on Propensity Scores12.3.3 Estimation of the Treatment Effect by Weighting by the Inverse of the Propensity Scores12.4 A Return to the Substantive Question 12.5 What to Read Next 13 Substantive Lessons from High-Quality Evaluations of Educational Interventions 13.1 Increasing School Enrollments13.1.1 Reduce Commuting Time13.1.2 Reduce Out-of-Pocket Educational Costs13.1.3 Reduce Opportunity Costs13.1.4 Does Increasing School Enrollment Necessarily Lead To Improved Long-Term Outcomes?13.2 Improving School Quality 13.2.1 Provide More or Better Educational Inputs 13.2.1.1 Provide More Books13.2.1.2 Teach Children in Smaller Classes13.2.1.3 Recruit Skilled Teachers or Provide Training to Enhance Teachers' Effectiveness13.2.2 Improve Incentives For Teachers13.2.3 Improving Incentives for Students13.2.4 Increase Families' Schooling Choices 13.3 Summing Up 14 Methodological Lessons from the Long Quest14.1 Be Clear About Your Theory of Action14.2 Learn about Culture, Rules, and Institutions in the Research Setting 14.3 Understand the Counterfactual 14.4 Worry about Selection Bias 14.5 Measure All Possible Important Outcomes14.6 Be On the Lookout for Longer-Term Effects14.7 Develop a Plan for Examining Impacts on Subgroups 14.8 Interpret Your Research Results Correctly 14.9 Pay Attention to Anomalous Results 14.10 Recognize That Good Research Always Raises New Questions 14.11 Final Words

"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 quantitativedata, using a panoply of innovative empirical approaches." --Eric A. Hanushek, Senior Fellow, Hoover Institution, Stanford University"Methods Matter 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 andexperienced 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 widerange 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 challengingresearchers 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 thatsignals 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 analyticalguidance 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 statisticalprogram on evaluation methods." --Elizabeth M. King, Director, Education, The World Bank