Waubonsee Community College

Public program evaluation, a statistical guide, Laura Langbein with Claire L. Felbinger

eng

Includes bibliographical references (pages 227-248) and index

illustrations

index present

non fiction

Public program evaluation

dictionariesbibliography

268793567

Laura Langbein with Claire L. Felbinger

a statistical guide

Designed to equip students and practitioners with the statistical skills needed to meet government standards regarding public program evaluation. Beginning with chapters on the overall context for successful program evaluations, this book explains the various forms of experimental validity that relate to evaluation designs

TC -- 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 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 DesignsRethinking 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 (ITSCG)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 -- For Further Reading -- Notes -- About the Authors -- Index