Methods Matter: Improving Causal Inference in Educational and Social Science ResearchEducational policy-makers around the world constantly make decisions about how to use scarce resources to improve the education of children. Unfortunately, their decisions are rarely informed by evidence on the consequences of these initiatives in other settings. Nor are decisions typically accompanied by well-formulated plans to evaluate their causal impacts. As a result, knowledge about what works in different situations has been very slow to accumulate. Over the last several decades, advances in research methodology, administrative record keeping, and statistical software have dramatically increased the potential for researchers to conduct compelling evaluations of the causal impacts of educational interventions, and the number of well-designed studies is growing. Written in clear, concise prose, Methods Matter: Improving Causal Inference in Educational and Social Science Research offers essential guidance for those who evaluate educational policies. Using numerous examples of high-quality studies that have evaluated the causal impacts of important educational interventions, the authors go beyond the simple presentation of new analytical methods to discuss the controversies surrounding each study, and provide heuristic explanations that are also broadly accessible. Murnane and Willett offer strong methodological insights on causal inference, while also examining the consequences of a wide variety of educational policies implemented in the U.S. and abroad. Representing a unique contribution to the literature surrounding educational research, this landmark text will be invaluable for students and researchers in education and public policy, as well as those interested in social science. |
Contents
| 3 | |
| 14 | |
3 Designing Research to Address Causal Questions | 26 |
4 InvestigatorDesigned Randomized Experiments | 40 |
5 Challenges in Designing Implementing and Learning from Randomized Experiments | 61 |
6 Statistical Power and Sample Size | 82 |
7 Experimental Research When Participants Are Clustered Within Intact Groups | 107 |
8 Using Natural Experiments to Provide Arguably Exogenous Treatment Variability | 135 |
10 Introducing InstrumentalVariables Estimation | 203 |
11 Using IVE to Recover the Treatment Effect in a QuasiExperiment | 265 |
12 Dealing with Bias in Treatment Effects Estimated from Nonexperimental Data | 286 |
13 Methodological Lessons from the Long Quest | 332 |
14 Substantive Lessons and New Questions | 350 |
| 369 | |
| 381 | |
9 Estimating Causal Effects Using a RegressionDiscontinuity Approach | 165 |
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Methods Matter:Improving Causal Inference in Educational and Social Science ... Richard J. Murnane,John B. Willett No preview available - 2010 |
Common terms and phrases
2SLS academic achievement annual family income assigned assumption Balsakhi base-year annual family bias career academies Catholic high school Catholic-school advantage causal impact causal inferences chapter child class-size Consequently control group covariates critical cut-off dichotomous difference educational attainment endogenous question predictor enrollment cohorts equal in expectation Equation evaluation example exogenous experimental conditions financial aid first-stage model forcing variable grade high-school seniors homoscedastic included instrument internal validity intervention intraclass correlation lottery Maimonides mathematics achievement MDRC ment multilevel model natural experiment null hypothesis NYSP observed obtained offer OLS estimate outcome p-value panel participants population private schools propensity scores question predictor random-assignment randomized experiment randomly reading achievement regression analysis relationship research design residual variance sample second-stage model standard error statistical power strategy student achievement t-statistic teachers tion treatment and control treatment effect treatment group Type I error unbiased estimate variation voucher