Public program evaluation : a statistical guide
Author(s)
Bibliographic Information
Public program evaluation : a statistical guide
M.E. Sharpe, c2006
- : pbk
Available at 5 libraries
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Note
Includes bibliographical references (p. 227-248) and index
Description and Table of Contents
- Volume
-
ISBN 9780765613660
Description
First Published in 2007. Routledge is an imprint of Taylor & Francis, an Informa company.
Table of Contents
- 1. What This Book Is About
- What Is Program Evaluation?
- * Types of Program Evaluations
- * Basic Characteristics of Program Evaluation
- * Relation of Program Evaluation to General Field of Policy Analysis
- * Assessing Government Performance: Program Evaluation and GPRA
- * A Brief History of Program Evaluation
- * What Comes Next
- * Key Concepts
- * Do It Yourself
- 2. Performance Measurement and Benchmarking
- Program Evaluation and Performance Measurement: What Is the Difference?
- * Benchmarking
- * Reporting Performance Results
- * Conclusion
- * Exercise: Try It Yourself!
- 3. 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
- 4. 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
- * Exercise: Try It Yourself
- * A Summary to Help
- 5. 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
- * Three Cool Examples of RFEs
- * Basic Concepts
- * Do It Yourself: Design a Randomized Field Experiment
- 6. The Quasi-Experiment
- Defining Quasi-Experimental Designs
- * The One-Shot Case Study
- * The Post-Test Only Comparison Group (PTCG)
- * The Pre-Test Post-Test Comparison Group (a.k.a. The Non-Equivalent Control Group)
- * The Pre-Test Post-Test (Single Group) Design
- * Single Interrupted Time-Series Design
- * The Interrupted Time-Series Comparison Group Design (TTSCG)
- * The Multiple Comparison Group Time-Series Design
- * Summary of Quasi-Experimental Design
- * Basic Concepts
- * Do It Yourself
- 7. The Non-Experimental Design: Variations on the Multiple Regression Theme
- What Is a Non-Experimental Design?
- * Back to Basics: The Workhorse Diagram
- * The Non-Experimental Workhorse Regression Equation
- * Data for the Workhorse Regression Equation
- * Interpreting Multiple Regression Output
- * Assumptions Needed to Believe That b is 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
- 8. Designing Useful Surveys for Evaluation
- Introduction
- * The Response Rate
- * How to Write Questions to Get Unbiased, Accurate, Informative Responses
- * Turning Responses into Useful Information
- * For Further Reading
- * Basic Concepts
- * On Your Own
- 9. 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
- * Conclusions
- * For Further Reading
- Notes
- * About the Authors
- * Index.
- Volume
-
: pbk ISBN 9780765613677
Description
First Published in 2007. Routledge is an imprint of Taylor & Francis, an Informa company.
Table of Contents
- Preface
- 1. What This Book Is About
- 2. Performance Measurement and Benchmarking
- 3. Defensible Program Evaluations: Four Types of Validity
- 4. Internal Validity
- 5. Randomized Field Experiments
- 6. The Quasi-Experiment
- 7. The Non-Experimental Design: Variations on the Multiple Regression Theme
- 8. Designing Useful Surveys for Evaluation
- 9. Summing It Up: Meta-Analysis
- Notes
- * About the Authors
- * Index.
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