Public program evaluation : a statistical guide

著者

書誌事項

Public program evaluation : a statistical guide

Laura Langbein, Claire L. Felbinger

M.E. Sharpe, c2006

  • : pbk

大学図書館所蔵 件 / 全5件

この図書・雑誌をさがす

注記

Includes bibliographical references (p. 227-248) and index

内容説明・目次

巻冊次

ISBN 9780765613660

内容説明

First Published in 2007. Routledge is an imprint of Taylor & Francis, an Informa company.

目次

  • 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.
巻冊次

: pbk ISBN 9780765613677

内容説明

First Published in 2007. Routledge is an imprint of Taylor & Francis, an Informa company.

目次

  • 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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