Häftad, Engelska, 2014
Counterfactuals and Causal Inference
Methods and Principles for Social Research
Av Stephen L. Morgan, Christopher Winship
Del i serien Analytical Methods for Social Research
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Beskrivning
Produktinformation
- Utgivningsdatum: 2014-11-17
- Mått: 176 x 253 x 28 mm
- Vikt: 990 g
- Format: Häftad
- Språk: Engelska
- Serie: Analytical Methods for Social Research
- Antal sidor: 515
- Upplaga: 2
- Förlag: Cambridge University Press
- ISBN: 9781107694163
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Stephen L. Morgan is the Bloomberg Distinguished Professor of Sociology and Education at Johns Hopkins University. He was previously the Jan Rock Zubrow '77 Professor in the Social Sciences and the director of the Center for the Study of Inequality at Cornell University. His current areas of interest include social stratification, the sociology of education, and quantitative methodology. He has published On the Edge of Commitment: Educational Attainment and Race in the United States (2005) and, as editor, the Handbook of Causal Analysis for Social Research (2013). Christopher Winship is the Diker–Tishman Professor of Sociology and member of the senior faculty of Harvard's Kennedy School of Government. Prior to coming to Harvard in 1992, he was Professor of Sociology and Statistics and by courtesy Economics at Northwestern University. His research focuses on statistical models for causal inference - most recently mechanisms and endogenous selection; how black clergy in Boston have worked with police to reduce youth violence; the effects of education on mental ability; pragmatism as the basis for a theory of action; the implications of advances in cognitive psychology for sociology; sociological approaches to how individuals understand justice. Since 1995 he has been editor of Sociological Methods and Research.
Recensioner i media
'The use of counterfactuals for causal inference has brought clarity to our reasoning about causality. And this second edition by Morgan and Winship will bring clarity to anyone trying to learn about the field. It is an excellent introduction to the topic, and a fine place to begin learning causal inference.' Tyler J. VanderWeele, Harvard University, Massachusetts
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