Identification Problems in the Social Sciences

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9780674442849: Identification Problems in the Social Sciences

This book provides a language and a set of tools for finding bounds on the predictions that social and behavioral scientists can logically make from nonexperimental and experimental data. The economist Charles Manski draws on examples from criminology, demography, epidemiology, social psychology, and sociology as well as economics to illustrate this language and to demonstrate the broad usefulness of the tools.

There are many traditional ways to present identification problems in econometrics, sociology, and psychometrics. Some of these are primarily statistical in nature, using concepts such as flat likelihood functions and nondistinct parameter estimates. Manski's strategy is to divorce identification from purely statistical concepts and to present the logic of identification analysis in ways that are accessible to a wide audience in the social and behavioral sciences. In each case, problems are motivated by real examples with real policy importance, the mathematics is kept to a minimum, and the deductions on identifiability are derived giving fresh insights.

Manski begins with the conceptual problem of extrapolating predictions from one population to some new population or to the future. He then analyzes in depth the fundamental selection problem that arises whenever a scientist tries to predict the effects of treatments on outcomes. He carefully specifies assumptions and develops his nonparametric methods of bounding predictions. Manski shows how these tools should be used to investigate common problems such as predicting the effect of family structure on children's outcomes and the effect of policing on crime rates.

Successive chapters deal with topics ranging from the use of experiments to evaluate social programs, to the use of case-control sampling by epidemiologists studying the association of risk factors and disease, to the use of intentions data by demographers seeking to predict future fertility. The book closes by examining two central identification problems in the analysis of social interactions: the classical simultaneity problem of econometrics and the reflection problem faced in analyses of neighborhood and contextual effects.

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Manski's provision of nonparametric bounds is insightful. The book also gives lucid and methodical exposition of the techniques involved and illustrative examples. Above all, its reminder that our conclusions can only be as good as our assumptions and its thoughtful remarks on the limitations of controlled experimentation in program evaluation research are incisive. Manski's self-reflective and circumspective critique and offer of solutions are surely commendable ...The book provides an illuminating discussion of a fundamental problem in social research.-Raymond Sin-Kwok Wong, Contemporary Sociology

About the Author:

Charles F. Manski is Board of Trustees Professor of Economics at Northwestern University.

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1.

Charles F. Manski
Editorial: HARVARD UNIVERSITY PRESS, United States (1999)
ISBN 10: 0674442849 ISBN 13: 9780674442849
Nuevos Paperback Cantidad: 10
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The Book Depository
(London, Reino Unido)
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Descripción HARVARD UNIVERSITY PRESS, United States, 1999. Paperback. Estado de conservación: New. Revised ed.. Language: English . Brand New Book. This text provides a language and a set of tools for finding bounds on the predictions that social and behavioural scientists can logically make from non-experimental and experimental data. Economist Charles Manski draws on examples from criminology, demography, epidemiology, social psychology and sociology as well as economics to illustrate this language and to demonstrate the broad usefulness of the tools. There are many traditional ways to present identification problems in econometrics, sociology and psychometrics. Some of these are primarily statistical in nature, using concepts such as flat likelihood functions and non-distinct parameter estimates. Manski s strategy is to divorce identification from purely statistical concepts and to present the logic of identification analysis in ways that are accessible to a wide audience in the social and behavioural sciences. In each case problems are motivated by real examples with real policy importance, the mathematics is kept to a minimum, and the deductions on identifiability are derived providing fresh insights. Manski begins with the conceptual problem of extrapolating predictions from one population to some new population or to the future. He then analyzes the fundamental selection problem that arises whenever a scientist tries to predict the effects of treatments on outcomes. He specifies assumptions and develops his non-parametric methods of bounding predictions. Manski shows how these tools should be used to investigate common problems such as predicting the effect of family structure on children s outcomes and the effect of policing on crime rates. Successive chapters deal with topics such as the use of experiments to evaluate social programmes, the use of case-control sampling by epidemiologists studying the association of risk factors and disease and the use of intentions data by demographers seeking to predict future fertility. The book closes by examining two central identification problems in the analysis of social interactions: the classical simultaneity problem of econometrics and the reflection problem faced in analyses of neighbourhood and contextual effects. Nº de ref. de la librería AAH9780674442849

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2.

Charles F. Manski
Editorial: HARVARD UNIVERSITY PRESS, United States (1999)
ISBN 10: 0674442849 ISBN 13: 9780674442849
Nuevos Paperback Cantidad: 10
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Descripción HARVARD UNIVERSITY PRESS, United States, 1999. Paperback. Estado de conservación: New. Revised ed.. Language: English . Brand New Book. This text provides a language and a set of tools for finding bounds on the predictions that social and behavioural scientists can logically make from non-experimental and experimental data. Economist Charles Manski draws on examples from criminology, demography, epidemiology, social psychology and sociology as well as economics to illustrate this language and to demonstrate the broad usefulness of the tools. There are many traditional ways to present identification problems in econometrics, sociology and psychometrics. Some of these are primarily statistical in nature, using concepts such as flat likelihood functions and non-distinct parameter estimates. Manski s strategy is to divorce identification from purely statistical concepts and to present the logic of identification analysis in ways that are accessible to a wide audience in the social and behavioural sciences. In each case problems are motivated by real examples with real policy importance, the mathematics is kept to a minimum, and the deductions on identifiability are derived providing fresh insights. Manski begins with the conceptual problem of extrapolating predictions from one population to some new population or to the future. He then analyzes the fundamental selection problem that arises whenever a scientist tries to predict the effects of treatments on outcomes. He specifies assumptions and develops his non-parametric methods of bounding predictions. Manski shows how these tools should be used to investigate common problems such as predicting the effect of family structure on children s outcomes and the effect of policing on crime rates. Successive chapters deal with topics such as the use of experiments to evaluate social programmes, the use of case-control sampling by epidemiologists studying the association of risk factors and disease and the use of intentions data by demographers seeking to predict future fertility. The book closes by examining two central identification problems in the analysis of social interactions: the classical simultaneity problem of econometrics and the reflection problem faced in analyses of neighbourhood and contextual effects. Nº de ref. de la librería AAH9780674442849

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Manski, Charles F.
Editorial: Harvard Univ Press
ISBN 10: 0674442849 ISBN 13: 9780674442849
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Descripción Harvard Univ Press. Estado de conservación: BRAND NEW. BRAND NEW Softcover A Brand New Quality Book from a Full-Time Bookshop in business since 1992!. Nº de ref. de la librería 2364073

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Descripción 1999. PAP. Estado de conservación: New. New Book. Shipped from US within 10 to 14 business days. Established seller since 2000. Nº de ref. de la librería TH-9780674442849

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Descripción Harvard University Press, 1999. PAP. Estado de conservación: New. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Nº de ref. de la librería WH-9780674442849

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Descripción 1999. PAP. Estado de conservación: New. New Book.Shipped from US within 10 to 14 business days. Established seller since 2000. Nº de ref. de la librería IB-9780674442849

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Descripción Harvard University Press 1999-03-15, Cambridge, Mass. |London, 1999. paperback. Estado de conservación: New. Nº de ref. de la librería 9780674442849

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Charles F. Manski
Editorial: HARVARD UNIVERSITY PRESS, United States (1999)
ISBN 10: 0674442849 ISBN 13: 9780674442849
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Descripción HARVARD UNIVERSITY PRESS, United States, 1999. Paperback. Estado de conservación: New. Revised ed.. Language: English . This book usually ship within 10-15 business days and we will endeavor to dispatch orders quicker than this where possible. Brand New Book. This text provides a language and a set of tools for finding bounds on the predictions that social and behavioural scientists can logically make from non-experimental and experimental data. Economist Charles Manski draws on examples from criminology, demography, epidemiology, social psychology and sociology as well as economics to illustrate this language and to demonstrate the broad usefulness of the tools. There are many traditional ways to present identification problems in econometrics, sociology and psychometrics. Some of these are primarily statistical in nature, using concepts such as flat likelihood functions and non-distinct parameter estimates. Manski s strategy is to divorce identification from purely statistical concepts and to present the logic of identification analysis in ways that are accessible to a wide audience in the social and behavioural sciences. In each case problems are motivated by real examples with real policy importance, the mathematics is kept to a minimum, and the deductions on identifiability are derived providing fresh insights. Manski begins with the conceptual problem of extrapolating predictions from one population to some new population or to the future. He then analyzes the fundamental selection problem that arises whenever a scientist tries to predict the effects of treatments on outcomes. He specifies assumptions and develops his non-parametric methods of bounding predictions. Manski shows how these tools should be used to investigate common problems such as predicting the effect of family structure on children s outcomes and the effect of policing on crime rates. Successive chapters deal with topics such as the use of experiments to evaluate social programmes, the use of case-control sampling by epidemiologists studying the association of risk factors and disease and the use of intentions data by demographers seeking to predict future fertility. The book closes by examining two central identification problems in the analysis of social interactions: the classical simultaneity problem of econometrics and the reflection problem faced in analyses of neighbourhood and contextual effects. Nº de ref. de la librería BTE9780674442849

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Manski, Charles F.
Editorial: Harvard University Press (1999)
ISBN 10: 0674442849 ISBN 13: 9780674442849
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Descripción Harvard University Press, 1999. Paperback. Estado de conservación: New. Never used!. Nº de ref. de la librería 0674442849

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ISBN 10: 0674442849 ISBN 13: 9780674442849
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Descripción 1999. Paperback. Estado de conservación: NEW. 9780674442849 This listing is a new book, a title currently in-print which we order directly and immediately from the publisher. For all enquiries, please contact Herb Tandree Philosophy Books directly - customer service is our primary goal. Nº de ref. de la librería HTANDREE0808217

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