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| Preface | |
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| What This Book Is About | |
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| What Is Program Evaluation? | |
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| Types of Program Evaluations | |
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| Basic Characteristics of Program Evaluation | |
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| Relation of Program Evaluation to the General Field of Policy Analysis | |
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| Assessing Government Performance: Program Evaluation and Performance Measurement | |
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| A Brief History of Program Evaluation | |
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| What Comes Next | |
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| Key Concepts | |
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| Do It Yourself | |
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| Defensible Program Evaluations: Four Types of Validity | |
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| Defining Defensibility | |
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| Types of Validity: Definitions | |
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| Types of Validity: Threats and Simple Remedies | |
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| Basic Concepts | |
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| Do It Yourself | |
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| Internal Validity | |
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| The Logic of Internal Validity | |
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| Making Comparisons: Cross Sections and Time Series | |
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| Threats to Internal Validity | |
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| Summary | |
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| Three Basic Research Designs | |
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| Rethinking Validity: The Causal Model Workhorse | |
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| Basic Concepts | |
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| Do It Yourself | |
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| A Summary of Threats to Internal Validity | |
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| Randomized Field Experiments | |
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| Basic Characteristics | |
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| Brief History | |
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| Caveats and Cautions About Randomized Experiments | |
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| Types of RFEs | |
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| Issues in Implementing RFEs | |
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| Threats to the Validity of RFEs: Internal Validity | |
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| Threats to the Validity of RFEs: External Validity | |
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| Threats to the Validity of RFEs: Measurement and Statistical Validity | |
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| Conclusion | |
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| Some Cool Examples of RFEs | |
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| Basic Concepts | |
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| Do It Yourself: Design a Randomized Field Experiment | |
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| The Quasi Experiment | |
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| Defining Quasi-Experimental Designs | |
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| The One-Shot Case Study | |
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| The Posttest-Only Comparison-Group (PTCG) Design | |
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| The Pretest-Posttest Comparison-Group (PTPTCG) (The Nonequivalent Control-Group) Design | |
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| The Pretest-Posttest (Single-Group) Design | |
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| The Single Interrupted Time-Series Design | |
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| The Interrupted Time-Series Comparison-Group (TTSCG) Design | |
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| The Multiple Comparison-Group Time-Series Design | |
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| Summary of Quasi-Experimental Design | |
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| Basic Concepts | |
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| Do It Yourself | |
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| The Nonexperimental Design: Variations on the Multiple Regression Theme | |
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| What Is a Nonexperimental Design? | |
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| Back to the Basics: The Workhorse Diagram | |
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| The Nonexperimental Workhorse Regression Equation | |
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| Data for the Workhorse Regression Equation | |
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| Interpreting Multiple Regression Output | |
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| Assumptions Needed to Believe That b Is a Valid Estimate of B [E(b) = B] | |
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| Assumptions Needed to Believe the Significance Test for b | |
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| What Happened to the R<sup>2</sub>? | |
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| Conclusion | |
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| Basic Concepts | |
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| Introduction to Stata | |
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| Do It Yourself: Interpreting Nonexperimental Results | |
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| Designing Useful Surveys for Evaluation | |
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| The Response Rate | |
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| How to Write Questions to Get Unbiased, Accurate, Informative Responses | |
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| Turning Responses into Useful Information | |
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| For Further Reading | |
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| Basic Concepts | |
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| Do It Yourself | |
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| Summing It Up: Meta-Analysis | |
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| What Is Meta-Analysis? | |
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| Example of a Meta-Analysis: Data | |
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| Example of a Meta-Analysis: Variables | |
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| Example of a Meta-Analysis: Data Analysis | |
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| The Role of Meta-Analysis in Program Evaluation and Causal Conclusions | |
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| For Further Reading | |
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| Index | |
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| About the Author | |