Propensity Score Analysis (inbunden)
Inbunden (Hardback)
Antal sidor
SAGE Publications, Inc
Fraser, Mark W.
colour illustrations, black & white tables, figures
234 x 188 x 30 mm
885 g
Antal komponenter
Propensity Score Analysis (inbunden)

Propensity Score Analysis

Statistical Methods and Applications

Inbunden Engelska, 2014-08-19
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With a strong focus on practical applications, the authors explore various strategies for employing PSA. In addition, they discuss the use of PSA with alternative types of data and limitations of PSA under a variety of constraints. Unlike the existing textbooks on program evaluation and causal inference, Propensity Score Analysis delves into statistical concepts, formulas, and models in the context of a robust and engaging focus on application.
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Recensioner i media

    Over the past 35 years, methods of program evaluation have undergone a significant change, and the researchers have recognized the need to develop more efficient approaches for assessing treatment effects from studies based on observational data and for evaluations based on quasi-experimental designs.

    Written by experts, this volume is updated and fully reflects the current changes to the field. It offers a systematic review of the history, origins, and statistical foundations of propensity score analysis,  and more.

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Övrig information

Shenyang Guo is the author of numerous research articles in child welfare, child mental health services, welfare, and health care. He has expertise in applying advanced statistical models to solving social welfare problems and has taught graduate courses that address survival analysis, hierarchical linear modeling, structural equation modeling, propensity score analysis, and program evaluation. In addition, Guo serves as the editor of SAGE Publications Advanced Quantitative Techniques in the Social Sciences Series and as a frequent reviewer for journals seeking a critique of advanced methodological analyses.

Guo is a fellow of American Academy of Social Work and Social Welfare. In addition, Guo serves on behalf of Washington University as the assistant vice chancellor for International AffairsGreater China and as the McDonnell International Academy ambassador to China working with Fudan University. He was appointed by Chinas Ministry of Education in 2016 as the Yangtze-River Chaired Professor at Xian Jiaotong University, China.

Mark W. Fraser, PhD, holds the Tate Distinguished Professorship at the School of Social Work, University of North Carolina where he serves as associate dean for research. He has won numerous awards for research and teaching, including the Aaron Rosen Award and the Distinguished Achievement Award from the Society for Social Work and Research. His work focuses on risk and resilience, child behavior, child and family services, and research methods. Dr. Fraser has published widely, and, in addition to Social Policy for Children and Families, is the co-author or editor of eight books. These include Families in Crisis, a study of intensive family-centered services, and Evaluating Family-Based Services, a text on methods for family research. In Risk and Resilience in Childhood, he and his colleagues describe resilience-based perspectives for child maltreatment, substance abuse, and other social problems. In Making Choices, Dr. Fraser and his co-authors outline a program to help children build sustaining social relationships. In The Context of Youth Violence, he explores violence from the perspective of resilience, risk, and protection, and in Intervention with Children and Adolescents, Dr. Fraser and his colleagues review advances in intervention knowledge for social and health problems. Intervention Research: Developing Social Programs describes the design and development of social programs. His most recent book is Propensity Score Analysis: Statistical Methods and Applications. Dr. Fraser serves as editor of the Journal of the Society for Social Work and Research. He is a fellow of the National Academies of Practice and the American Academy of Social Work and Social Welfare.


Chapter 1: Introduction Chapter 2: Counterfactual Framework and Assumptions Chapter 3: Conventional Methods for Data Balancing Chapter 4: Sample Selection and Related Models Chapter 5: Propensity Score Matching and Related Models Chapter 6: Propensity Score Subclassification Chapter 7: Propensity Score Weighting Chapter 8: Matching Estimators Chapter 9: Propensity Score Analysis with Nonparametric Regression Chapter 10: Propensity Score Analysis of Categorical or Continuous Treatments Chapter 11: Selection Bias and Sensitivity Analysis Chapter 12: Concluding Remarks