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Methods matter : improving causal inference in educational and social science research / Richard J. Murnane, John B. Willett.

By: Murnane, Richard J.
Contributor(s): Willett, John B.
Material type: materialTypeLabelBookPublisher: Oxford ; New York : Oxford University Press, 2011Description: xv, 397 p. : ill. ; 25 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9780199753864 (hardback); 0199753865 (hardback).Subject(s): Education -- Research -- Methodology | Quantitative researchDDC classification: 370.72 MUR
Contents:
The challenge for educational research -- The importance of theory -- Designing research to address causal questions -- Investigator-designed randomized experiments -- Challenges in designing, implementing, and learning from randomized experiments -- Statistical power and sample size -- Experimental research when participants are clustered within intact groups -- Using natural experiments to provide "arguably exogenous" treatment variability -- Estimating causal effects using a regression-discontinuity approach -- Introducing instrumental-variables estimation -- Using IVE to recover the treatment effect in a quasi-experiment -- Dealing with bias in treatment effects estimated from nonexperimental data -- Methodological lessons from the long quest -- Substantive lessons and new questions.
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Item type Current location Call number Status Date due Barcode
Book Book Indian Institute for Human Settlements, Bangalore
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370.72 MUR 013385 (Browse shelf) Available 013385

Includes bibliographical references and index.

The challenge for educational research -- The importance of theory -- Designing research to address causal questions -- Investigator-designed randomized experiments -- Challenges in designing, implementing, and learning from randomized experiments -- Statistical power and sample size -- Experimental research when participants are clustered within intact groups -- Using natural experiments to provide "arguably exogenous" treatment variability -- Estimating causal effects using a regression-discontinuity approach -- Introducing instrumental-variables estimation -- Using IVE to recover the treatment effect in a quasi-experiment -- Dealing with bias in treatment effects estimated from nonexperimental data -- Methodological lessons from the long quest -- Substantive lessons and new questions.

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