Propensity Score Analysis: Statistical Methods and Applications (Advanced Quantitative Techniques in the Social Sciences). Dieser Artikel ist nicht verfügbar.Propensity Score Analysis: Statistical Methods and Applications (Advanced Quantitative Techniques in the Social Sciences)

Propensity Score Analysis provides readers with a systematic review of the origins, history, and statistical foundations of PSA and illustrates how it can be used for solving evaluation problems. With a strong focus on practical applications, the authors explore various types of data and evaluation problems related to, strategies for employing, and the limitations of PSA. Unlike the existing textbooks on program evaluation, Propensity Score Analysis delves into statistical concepts, formulas, and models underlying the application.

Key Features

• Presents key information on model derivations
• Summarizes complex statistical arguments but omits their proofs
• Links each method found in this book to specific Stata programs and provides empirical examples
• Guides readers using two conceptual frameworks: the Neyman-Rubin counterfactual framework and the Heckman econometric model of causality
• Contains examples representing real challenges commonly found in social behavioral research
• Utilizes data simulation and Monte Carlo studies to illustrate key points
• Presents descriptions of new statistical approaches necessary for understanding the four evaluation methods incorporated throughout the text

Intended Audience

This text is appropriate for graduate and doctoral students taking Evaluation, Quantitative Methods, Survey Research, and Research Design courses across business, social work, public policy, psychology, sociology, and health/medicine disciplines.

Shenyang Guo, PhD, is the Kuralt Distinguished Professor at the School of Social Work, University of North Carolina. The author of numerous articles on statistical methods and research reports in child welfare, child mental health services, welfare, and health care, Guo has expertise in applying advanced statistical models to solving social welfare problems and has taught graduate courses on event history analysis, hierarchical linear modeling, growth curve modeling, and program evaluation. He has given many invited workshops on statistical methods-including event history analysis and propensity score matching-at the NIH Summer Institute, Children's Bureau, and at conferences of the Society of Social Work and Research. He led the data analysis planning for the National Survey of Child and Adolescent Well-Being (NSCAW) longitudinal analysis. Mark W. Fraser, PhD, holds the Tate Distinguished Professorship at the School of Social Work, University of North Carolina at Chapel Hill, where he serves as associate dean for research. He has written numerous chapters and articles on risk and resilience, child behavior, child and family services, and research methods. With colleagues, he is the co-author or editor of eight books, including Families in Crisis, Evaluating Family-Based Services, Risk and Resilience in Childhood, Making Choices, The Context of Youth Violence, and Intervention with Children and Adolescents. His award-winning text Social Policy for Children and Families reviews the bases for public policy in child welfare, juvenile justice, mental health, developmental disabilities, and health. His most recent book, Intervention Research: Developing Social Programs, describes a design perspective on the development of innovative social and health programs.