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Identification Problems in the Social Sciences
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MYR 268
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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.
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- 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.
| Publisher | Harvard University Press |
| Publication date | May 29, 2019 |
| Edition | Revised |
| Language | English |
| Print length | 184 pages |
| ISBN-10 | 0674442849 |
| ISBN-13 | 978-0674442849 |
| Item Weight | 8 ounces (226.8 grams) |
| Dimensions | 6 x 0.46 x 9 inches (15.2 x 1.2 x 22.9 cm) |
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Identification Problems in the Social Sciences
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Charles F. Manski Mathematics Editorial Review
The "Identification Problems in the Social Sciences Revised Edition" is a highly recommended book for anyone who wants to understand statistical identification in social sciences. The book is easy to read and presents important topics in statistical identification that are often overlooked. It provides a nonparametric introduction to identification issues and is written for social sciences students. The book is opinionated, but this is not necessarily a bad thing as it adds to the overall understanding of the subject.
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Ulas Produk Ini
Kongsikan pandangan anda dengan pelanggan lain
Kebaikan
- Easy to read
- Addresses important topics in statistical identification
- Recommended for those who want to formalize their thoughts and test models with real-life data
Keburukan
- Opinionated (may not suit everyone's preferences)
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MYR 268
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Ciri & Faedah
- Provides tools for predicting from experimental and nonexperimental data.
- Utilizes real-world examples across various social sciences for practical insights.
- Focuses on the conceptual issues in extrapolating predictions.
- Minimizes complex mathematics for broader accessibility.
- Addresses key problems in social interactions and policy relevance.
- Ideal for social and behavioral scientists looking to enhance their analytical capabilities.
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