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Featuring engaging examples from diverse disciplines, this book explains how to use modern approaches to quasi-experimentation to derive credible estimates of treatment effects under the demanding constraints of field settings. Foremost expert Charles S. Reichardt provides an in-depth examination of the design and statistical analysis of pretest-posttest, nonequivalent groups, regression discontinuity, and interrupted time-series designs. He details their relative strengths and weaknesses and offers practical advice about their use. Comparing quasi-experiments to randomized experiments,…mehr
Featuring engaging examples from diverse disciplines, this book explains how to use modern approaches to quasi-experimentation to derive credible estimates of treatment effects under the demanding constraints of field settings. Foremost expert Charles S. Reichardt provides an in-depth examination of the design and statistical analysis of pretest-posttest, nonequivalent groups, regression discontinuity, and interrupted time-series designs. He details their relative strengths and weaknesses and offers practical advice about their use. Comparing quasi-experiments to randomized experiments, Reichardt discusses when and why the former might be a better choice than the latter in the face of the contingencies that are likely to arise in practice. Modern methods for elaborating a research design to remove bias from estimates of treatment effects are described, as are tactics for dealing with missing data and noncompliance with treatment assignment. Throughout, mathematical equations are translated into words to enhance accessibility. Adding to its discussion of prototypical quasi-experiments, the book also provides a complete typology of quasi-experimental design options to help the reader craft the best research design to fit the circumstances of a given study.
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Charles S. Reichardt, PhD, is Professor of Psychology at the University of Denver. He is an elected fellow of the American Psychological Society, an elected member of the Society of Multivariate Experimental Psychology, and a recipient of the Robert Perloff President's Prize from the Evaluation Research Society and the Jeffrey S. Tanaka Award from the Society of Multivariate Experimental Psychology. Dr. Reichardt's research focuses on quasi-experimentation.
Inhaltsangabe
1. Introduction Overview 1.1 Introduction 1.2 The Definition of Quasi-Experiment 1.3 Why Study Quasi-Experiments 1.4 Overview of the Volume 1.5 Conclusions 1.6 Suggested Reading 2. Cause and Effect Overview 2.1 Introduction 2.2 Practical Comparisons and Confounds 2.3 The Counterfactual Definition 2.4 The Stable-Unit-Treatment-Value Assumption (SUTVA) 2.5 The Causal Question Being Addressed 2.6 Conventions 2.7 Conclusions 2.8 Suggested Reading 3. Threats to Validity Overview 3.1 Introduction 3.2 The Size of an Effect 3.3 Construct Validity 3.4 Internal Validity 3.5 Statistical Conclusion Validity 3.6 External Validity 3.7 Trade-offs among Types of Validity 3.8 A Focus on Internal and Statistical Conclusion Validity 3.9 Conclusions 3.10 Suggested Reading 4. Randomized Experiments Overview 4.1 Introduction 4.2 Between-Groups Randomized Experiments 4.3 Examples of Randomized Experiments Conducted in the Field 4.4 Selection Differences 4.5 Analysis of Data from the Posttest-Only Randomized Experiment 4.6 Analysis of Data from the Pretest-Posttest Randomized Experiment 4.7 Noncompliance with Treatment Assignment 4.8 Missing Data and Attrition 4.9 Cluster-Randomized Experiments 4.10 Other Threats to Validity in Randomized Experiments 4.11 Strengths and Weaknesses 4.12 Conclusions 4.13 Suggested Reading 5. One-Group Posttest-Only Designs Overview 5.1 Introduction 5.2 Examples of One-Group Posttest-Only Designs 5.3 Strengths and Weaknesses 5.4 Conclusions 5.5 Suggested Reading 6. Pretest-Posttest Designs Overview 6.1 Introduction 6.2 Examples of Pretest-Posttest Designs 6.3 Threats to Internal Validity 6.4 Design Variations 6.5 Strengths and Weaknesses 6.6 Conclusions 6.7 Suggested Reading 7. Nonequivalent Group Designs Overview 7.1 Introduction 7.2 Two Basic Nonequivalent Group Designs 7.3 Change-Score Analysis 7.4 Analysis of Covariance 7.5 Matching and Blocking 7.6 Propensity Scores 7.7 Instrumental Variables 7.8 Selection Models 7.9 Sensitivity Analyses and Tests of Ignorability 7.10 Other Threats to Internal Validity besides Selection Differences 7.11 Alternative Nonequivalent Group Designs 7.12 Empirical Evaluations and Best Practices 7.13 Strengths and Weaknesses 7.14 Conclusions 7.15 Suggested Reading 8. Regression Discontinuity Designs Overview 8.1 Introduction 8.2 The Quantitative Assignment Variable 8.3 Statistical Analysis 8.4 Fuzzy Regression Discontinuity 8.5 Threats to Internal Validity 8.6 Supplemented Designs 8.7 Cluster Regression Discontinuity Designs 8.8 Strengths and Weaknesses 8.9 Conclusions 8.10 Suggested Reading 9. Interrupted Time-Series Designs Overview 9.1 Introduction 9.2 The Temporal Pattern of the Treatment Effect 9.3 Two Versions of the Design 9.4 The Statistical Analysis of Data When N = 1 9.5 The Statistical Analysis of Data When N Is Large 9.6 Threats to Internal Validity 9.7 Design Supplements I: Multiple Interventions 9.8 Design Supplements II: Basic Comparative ITS Designs 9.9 Design Supplements III: Comparative ITS Designs with Multiple Treatments 9.10 Single-Case Designs 9.11 Strengths and Weaknesses 9.12 Conclusions 9.13 Suggested Reading 10. A Typology of Comparisons Overview 10.1 Introduction 10.2 The Principle of Parallelism 10.3 Comparisons across Participants 10.4 Comparisons across Times 10.5 Comparisons across Settings 10.6 Comparisons across Outcome Measures 10.7 Within- and Between-Subject Designs 10.8 A Typology of Comparisons 10.9 Random Assignment to Treatment Conditions 10.10 Assignment to Treatment Conditions Based on an Explicit Quantitative Ordering 10.11 Nonequivalent Assignment to Treatment Conditions 10.12 Credibility and Ease of Implementation 10.13 The Most Commonly Used Comparisons 10.14 Conclusions 10.15 Suggested Reading 11. Methods of Design Elaboration Overview 11.1 Introduction 11.2 Three Methods of Design Elaboration 11.3 The Four Size-of-Effect Factors as Sources for the Two Estimates in Design Elaboration 11.4 Conclusions 11.5 Suggested Reading 12. Unfocused Design Elaboration and Pattern Matching Overview 12.1 Introduction 12.2 Four Examples of Unfocused Design Elaboration 12.3 Pattern Matching 12.4 Conclusions 12.5 Suggested Reading 13. Principles of Design and Analysis for Estimating Effects Overview 13.1 Introduction 13.2 Design Trumps Statistics 13.3 Customized Designs 13.4 Threats to Validity 13.5 The Principle of Parallelism 13.6 The Typology of Simple Comparisons 13.7 Pattern Matching and Design Elaborations 13.8 Size of Effects 13.9 Bracketing Estimates of Effects 13.10 Critical Multiplism 13.11 Mediation 13.12 Moderation 13.13 Implementation 13.14 Qualitative Research Methods 13.15 Honest and Open Reporting of Results 13.16 Conclusions 13.17 Suggested Reading Appendix: The Problems of Overdetermination and Preemption A.1 The Problem of Overdetermination A.2 The Problem of Preemption References Glossary Author Index Subject Index About the Author
1. Introduction Overview 1.1 Introduction 1.2 The Definition of Quasi-Experiment 1.3 Why Study Quasi-Experiments 1.4 Overview of the Volume 1.5 Conclusions 1.6 Suggested Reading 2. Cause and Effect Overview 2.1 Introduction 2.2 Practical Comparisons and Confounds 2.3 The Counterfactual Definition 2.4 The Stable-Unit-Treatment-Value Assumption (SUTVA) 2.5 The Causal Question Being Addressed 2.6 Conventions 2.7 Conclusions 2.8 Suggested Reading 3. Threats to Validity Overview 3.1 Introduction 3.2 The Size of an Effect 3.3 Construct Validity 3.4 Internal Validity 3.5 Statistical Conclusion Validity 3.6 External Validity 3.7 Trade-offs among Types of Validity 3.8 A Focus on Internal and Statistical Conclusion Validity 3.9 Conclusions 3.10 Suggested Reading 4. Randomized Experiments Overview 4.1 Introduction 4.2 Between-Groups Randomized Experiments 4.3 Examples of Randomized Experiments Conducted in the Field 4.4 Selection Differences 4.5 Analysis of Data from the Posttest-Only Randomized Experiment 4.6 Analysis of Data from the Pretest-Posttest Randomized Experiment 4.7 Noncompliance with Treatment Assignment 4.8 Missing Data and Attrition 4.9 Cluster-Randomized Experiments 4.10 Other Threats to Validity in Randomized Experiments 4.11 Strengths and Weaknesses 4.12 Conclusions 4.13 Suggested Reading 5. One-Group Posttest-Only Designs Overview 5.1 Introduction 5.2 Examples of One-Group Posttest-Only Designs 5.3 Strengths and Weaknesses 5.4 Conclusions 5.5 Suggested Reading 6. Pretest-Posttest Designs Overview 6.1 Introduction 6.2 Examples of Pretest-Posttest Designs 6.3 Threats to Internal Validity 6.4 Design Variations 6.5 Strengths and Weaknesses 6.6 Conclusions 6.7 Suggested Reading 7. Nonequivalent Group Designs Overview 7.1 Introduction 7.2 Two Basic Nonequivalent Group Designs 7.3 Change-Score Analysis 7.4 Analysis of Covariance 7.5 Matching and Blocking 7.6 Propensity Scores 7.7 Instrumental Variables 7.8 Selection Models 7.9 Sensitivity Analyses and Tests of Ignorability 7.10 Other Threats to Internal Validity besides Selection Differences 7.11 Alternative Nonequivalent Group Designs 7.12 Empirical Evaluations and Best Practices 7.13 Strengths and Weaknesses 7.14 Conclusions 7.15 Suggested Reading 8. Regression Discontinuity Designs Overview 8.1 Introduction 8.2 The Quantitative Assignment Variable 8.3 Statistical Analysis 8.4 Fuzzy Regression Discontinuity 8.5 Threats to Internal Validity 8.6 Supplemented Designs 8.7 Cluster Regression Discontinuity Designs 8.8 Strengths and Weaknesses 8.9 Conclusions 8.10 Suggested Reading 9. Interrupted Time-Series Designs Overview 9.1 Introduction 9.2 The Temporal Pattern of the Treatment Effect 9.3 Two Versions of the Design 9.4 The Statistical Analysis of Data When N = 1 9.5 The Statistical Analysis of Data When N Is Large 9.6 Threats to Internal Validity 9.7 Design Supplements I: Multiple Interventions 9.8 Design Supplements II: Basic Comparative ITS Designs 9.9 Design Supplements III: Comparative ITS Designs with Multiple Treatments 9.10 Single-Case Designs 9.11 Strengths and Weaknesses 9.12 Conclusions 9.13 Suggested Reading 10. A Typology of Comparisons Overview 10.1 Introduction 10.2 The Principle of Parallelism 10.3 Comparisons across Participants 10.4 Comparisons across Times 10.5 Comparisons across Settings 10.6 Comparisons across Outcome Measures 10.7 Within- and Between-Subject Designs 10.8 A Typology of Comparisons 10.9 Random Assignment to Treatment Conditions 10.10 Assignment to Treatment Conditions Based on an Explicit Quantitative Ordering 10.11 Nonequivalent Assignment to Treatment Conditions 10.12 Credibility and Ease of Implementation 10.13 The Most Commonly Used Comparisons 10.14 Conclusions 10.15 Suggested Reading 11. Methods of Design Elaboration Overview 11.1 Introduction 11.2 Three Methods of Design Elaboration 11.3 The Four Size-of-Effect Factors as Sources for the Two Estimates in Design Elaboration 11.4 Conclusions 11.5 Suggested Reading 12. Unfocused Design Elaboration and Pattern Matching Overview 12.1 Introduction 12.2 Four Examples of Unfocused Design Elaboration 12.3 Pattern Matching 12.4 Conclusions 12.5 Suggested Reading 13. Principles of Design and Analysis for Estimating Effects Overview 13.1 Introduction 13.2 Design Trumps Statistics 13.3 Customized Designs 13.4 Threats to Validity 13.5 The Principle of Parallelism 13.6 The Typology of Simple Comparisons 13.7 Pattern Matching and Design Elaborations 13.8 Size of Effects 13.9 Bracketing Estimates of Effects 13.10 Critical Multiplism 13.11 Mediation 13.12 Moderation 13.13 Implementation 13.14 Qualitative Research Methods 13.15 Honest and Open Reporting of Results 13.16 Conclusions 13.17 Suggested Reading Appendix: The Problems of Overdetermination and Preemption A.1 The Problem of Overdetermination A.2 The Problem of Preemption References Glossary Author Index Subject Index About the Author
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