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, an...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, MA
Harvard University Press
[2022]
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| Online-Zugang: | FAW01 FAB01 FCO01 FHA01 FKE01 FLA01 UPA01 UBG01 Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | Description based on online resource; title from PDF title page (publisher's Web site, viewed 31. Jan 2022) |
| Beschreibung: | 1 online resource (194 pages) |
| ISBN: | 9780674265790 |
| DOI: | 10.4159/9780674265790 |
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| 520 | |a 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. | ||
| 520 | |a 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. | ||
| 520 | |a 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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Datensatz im Suchindex
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| index_date | 2024-07-03T19:08:08Z |
| indexdate | 2024-07-10T09:22:15Z |
| institution | BVB |
| isbn | 9780674265790 |
| language | English |
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| spelling | Manski, Charles F. Verfasser aut Identification Problems in the Social Sciences Charles F. Manski Cambridge, MA Harvard University Press [2022] © 1999 1 online resource (194 pages) txt rdacontent c rdamedia cr rdacarrier Description based on online resource; title from PDF title page (publisher's Web site, viewed 31. Jan 2022) 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 In English BUSINESS & ECONOMICS / Econometrics bisacsh Estimation theory Social sciences Statistical methods https://doi.org/10.4159/9780674265790 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Manski, Charles F. Identification Problems in the Social Sciences BUSINESS & ECONOMICS / Econometrics bisacsh Estimation theory Social sciences Statistical methods |
| title | Identification Problems in the Social Sciences |
| title_auth | Identification Problems in the Social Sciences |
| title_exact_search | Identification Problems in the Social Sciences |
| title_exact_search_txtP | Identification Problems in the Social Sciences |
| title_full | Identification Problems in the Social Sciences Charles F. Manski |
| title_fullStr | Identification Problems in the Social Sciences Charles F. Manski |
| title_full_unstemmed | Identification Problems in the Social Sciences Charles F. Manski |
| title_short | Identification Problems in the Social Sciences |
| title_sort | identification problems in the social sciences |
| topic | BUSINESS & ECONOMICS / Econometrics bisacsh Estimation theory Social sciences Statistical methods |
| topic_facet | BUSINESS & ECONOMICS / Econometrics Estimation theory Social sciences Statistical methods |
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