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Public Program Evaluation: A Statistical Guide, Second Edition
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Comments/Reviews
Description: Readable and comprehensive, this text is designed to equip students and practitioners with the statistical skills needed to meet government standards regarding public program evaluation. Even those with little or no statistical training will find the explanations clear, with many illustrative examples, case studies, and applications.
Far more than a cookbook of statistical techniques, the book begins with chapters on the overall context for successful program evaluations, and carefully explains statistical methods--and threats to internal and statistical validity--that correspond to each evaluation design. Laura Langbein then presents a variety of methods for program analysis, and advises readers on how to select the mix of methods most appropriate for the issues they deal with-always balancing methodology with the need for generality, the size of the evaluator's budget, the availability of data, and the need for quick results.
Among this text's many important features: maintains a practical focus on doing evaluation; integrates research design with corresponding statistical/econometric estimation methods; uses examples from many policy fields, not just social services; uses examples from domestic programs as well as developing countries; links program evaluation to the larger field of policy analysis.
Selected Contents:
Preface
1. What This Book Is About
What Is Program Evaluation?
Types of Program Evaluations
Basic Characteristics of Program Evaluation
Relation of Program Evaluation to the General Field of Policy Analysis
Assessing Government Performance: Program Evaluation and Performance Measurement
A Brief History of Program Evaluation
What Comes Next
Key Concepts
Do It Yourself
2. Defensible Program Evaluations: Four Types of Validity
Defining Defensibility
Types of Validity: Definitions
Types of Validity: Threats and Simple Remedies
Basic Concepts
Do It Yourself
3. Internal Validity
The Logic of Internal Validity
Making Comparisons: Cross Sections and Time Series
Threats to Internal Validity
Summary
Three Basic Research Designs
Rethinking Validity: The Causal Model Workhorse
Basic Concepts
Do It Yourself
A Summary of Threats to Internal Validity
4. Randomized Field Experiments
Basic Characteristics
Brief History
Caveats and Cautions About Randomized Experiments
Types of RFEs
Issues in Implementing RFEs
Threats to the Validity of RFEs: Internal Validity
Threats to the Validity of RFEs: External Validity
Threats to the Validity of RFEs: Measurement and Statistical Validity
Conclusion
Some Cool Examples of RFEs
Basic Concepts
Do It Yourself: Design a Randomized Field Experiment
5. The Quasi Experiment
Defining Quasi-Experimental Designs
The One-Shot Case Study
The Posttest-Only Comparison-Group (PTCG) Design
The Pretest-Posttest Comparison-Group (PTPTCG) (The Nonequivalent Control-Group) Design
The Pretest-Posttest (Single-Group) Design
The Single Interrupted Time-Series Design
The Interrupted Time-Series Comparison-Group (ITSCG) Design
The Multiple Comparison-Group Time-Series Design
Summary of Quasi-Experimental Design
Basic Concepts
Do It Yourself
6. The Nonexperimental Design: Variations on the Multiple Regression Theme
What Is a Nonexperimental Design?
Back to the Basics: The Workhorse Diagram
The Nonexperimental Workhorse Regression Equation
Data for the Workhorse Regression Equation
Interpreting Multiple Regression Output
Assumptions Needed to Believe That b Is a Valid Estimate of B [E (b ) = B ]
Assumptions Needed to Believe the Significance Test for b
What Happened to the R2 ?
Conclusion
Basic Concepts
Introduction to Stata
Do It Yourself: Interpreting Nonexperimental Results
7. Designing Useful Surveys for Evaluation
The Response Rate
How to Write Questions to Get Unbiased, Accurate, Informative Responses
Turning Responses into Useful Information
For Further Reading
Basic Concepts
Do It Yourself
8. Summing It Up: Meta-Analysis
What Is Meta-Analysis?
Example of a Meta-Analysis: Data
Example of a Meta-Analysis: Variables
Example of a Meta-Analysis: Data Analysis
The Role of Meta-Analysis in Program Evaluation and Causal Conclusions
For Further Reading
Index
About the Author
Comment(s): "Langbein covers the key concepts of program evaluation and ties them to the statistical approaches that can be used to implement evaluations. This a well-written book that will be accessible to students and practitioners with interests in program evaluation. The many examples and case studies will keep students engaged in the topic." -- Maureen A. Pirog, Editor-in-Chief, Journal of Policy Analysis and Management, Rudy Professor of Public and Environmental Affairs, Indiana University
" Public Program Evaluation fills a gap that most texts leave unfilled. Other books may discuss what constitutes a good evaluation design, but they don't help the student learn how to actually carry out the tests that are needed to produce the answers. Langbein's text does that and, in so doing, helps students think more rigorously about evaluation." -- John Mendeloff, University of Pittsburgh
"This is an authoritative, comprehensive, and highly readable book. Its emphasis is on practical, timely research designs for program evaluation without losing sight of the importance of validity issues. The new edition, with the addition of many useful, practical exercises drawn from actual cases, is a valuable addition to the literature for students and practitioners." -- Barbara Carroll, McMaster University
Review(s): "This book provides an extensive discussion of the variety of research designs (and corresponding statistical tools) used to assess program impact and thoughtfully addresses evaluation's place in public administration and policy making. ... This book provides a valuable bridge between textbooks devoted to either research design or statistics. This book should provide useful guidance to many program evaluators and public managers." -- American Journal of Evaluation (on the previous edition)
    
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