Public Program Evaluation: A Statistical Guide

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Edition: 2nd
Format: Paperback
Pub. Date: 2012-07-15
Publisher(s): Routledge
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Summary

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.

Table of Contents

Prefacep. ix
What This Book Is Aboutp. 3
What Is Program Evaluation?p. 3
Types of Program Evaluationsp. 8
Basic Characteristics of Program Evaluationp. 13
Relation of Program Evaluation to the General Field of Policy Analysisp. 15
Assessing Government Performance: Program Evaluation and Performance Measurementp. 15
A Brief History of Program Evaluationp. 17
What Comes Nextp. 19
Key Conceptsp. 20
Do It Yourselfp. 20
Defensible Program Evaluations: Four Types of Validityp. 26
Defining Defensibilityp. 26
Types of Validity: Definitionsp. 27
Types of Validity: Threats and Simple Remediesp. 28
Basic Conceptsp. 47
Do It Yourselfp. 48
Internal Validityp. 51
The Logic of Internal Validityp. 51
Making Comparisons: Cross Sections and Time Seriesp. 54
Threats to Internal Validityp. 55
Summaryp. 63
Three Basic Research Designsp. 64
Rethinking Validity: The Causal Model Workhorsep. 66
Basic Conceptsp. 68
Do It Yourselfp. 69
A Summary of Threats to Internal Validityp. 70
Randomized Field Experimentsp. 73
Basic Characteristicsp. 73
Brief Historyp. 74
Caveats and Cautions About Randomized Experimentsp. 76
Types of RFEsp. 79
Issues in Implementing RFEsp. 92
Threats to the Validity of RFEs: Internal Validityp. 96
Threats to the Validity of RFEs: External Validityp. 100
Threats to the Validity of RFEs: Measurement and Statistical Validityp. 101
Conclusionp. 101
Some Cool Examples of RFEsp. 102
Basic Conceptsp. 103
Do It Yourself: Design a Randomized Field Experimentp. 104
The Quasi Experimentp. 110
Defining Quasi-Experimental Designsp. 110
The One-Shot Case Studyp. 111
The Posttest-Only Comparison-Group (PTCG) Designp. 113
The Pretest-Posttest Comparison-Group (PTPTCG) (The Nonequivalent Control-Group) Designp. 119
The Pretest-Posttest (Single-Group) Designp. 123
The Single Interrupted Time-Series Designp. 125
The Interrupted Time-Series Comparison-Group (TTSCG) Designp. 131
The Multiple Comparison-Group Time-Series Designp. 134
Summary of Quasi-Experimental Designp. 135
Basic Conceptsp. 136
Do It Yourselfp. 137
The Nonexperimental Design: Variations on the Multiple Regression Themep. 143
What Is a Nonexperimental Design?p. 143
Back to the Basics: The Workhorse Diagramp. 144
The Nonexperimental Workhorse Regression Equationp. 146
Data for the Workhorse Regression Equationp. 148
Interpreting Multiple Regression Outputp. 149
Assumptions Needed to Believe That b Is a Valid Estimate of B [E(b) = B]p. 164
Assumptions Needed to Believe the Significance Test for bp. 184
What Happened to the R2?p. 190
Conclusionp. 191
Basic Conceptsp. 192
Introduction to Statap. 194
Do It Yourself: Interpreting Nonexperimental Resultsp. 197
Designing Useful Surveys for Evaluationp. 209
The Response Ratep. 210
How to Write Questions to Get Unbiased, Accurate, Informative Responsesp. 217
Turning Responses into Useful Informationp. 224
For Further Readingp. 233
Basic Conceptsp. 233
Do It Yourselfp. 234
Summing It Up: Meta-Analysisp. 239
What Is Meta-Analysis?p. 239
Example of a Meta-Analysis: Datap. 240
Example of a Meta-Analysis: Variablesp. 241
Example of a Meta-Analysis: Data Analysisp. 242
The Role of Meta-Analysis in Program Evaluation and Causal Conclusionsp. 243
For Further Readingp. 244
Indexp. 247
About the Authorp. 253
Table of Contents provided by Ingram. All Rights Reserved.

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