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Author:   Charles S. Reichardt
Publisher:   Guilford Publications
Imprint:   Guilford Press
Weight:   0.654kg
ISBN:  

9781462540204

ISBN 10:   1462540201
Pages:   361
Publication Date:   26 September 2019
Audience:   College/higher education ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   To order  
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Table of Contents

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

Reviews

This book represents an important contribution to the literature on research designs that may be implemented when randomized experiments are not feasible or are limited. Covering the full range of design alternatives, this is the first text that fuses important recent advances from statistics and econometrics into Campbell's pioneering approach. A notable feature is Reichardt's careful attention to issues that arise in each research design; he offers innovative design and analysis strategies that can minimize these issues and permit the strongest possible conclusions from research. Clearly written, this text is an outstanding choice for courses focusing on key issues of research design, and is suitable for graduate students with only a basic background in statistics. Established researchers will find it to be a valuable reference that offers new insights for strengthening research designs so that they yield the most credible possible evidence. --Stephen G. West, PhD, Department of Psychology, Arizona State University This book not only compiles a comprehensive list of methods on quasi-causal design, but also problematizes causes of biases even in perfectly executed experimental and quasi-experimental designs. The author's take on the ways in which quasi-experiments could potentially render better results than randomized experiments is refreshing and important. Professors will want to discuss this book in their classes. I highly recommend it for students and even more experienced researchers--the author highlights the fundamentals of each approach along with its strengths and limitations. --Manuel Gonzalez Canche, PhD, Higher Education, Quantitative Methods, and Education Policy Divisions, Graduate School of Education, University of Pennsylvania A 'must read.' After a thorough presentation of the strengths of randomized experiments, Reichardt provides a remarkably up-to-date review and synthesis of current thinking on the best, most useful alternatives. Notably, he uses simple and direct language to explain key concepts of the 'counterfactual outcomes' approach for estimating causal effects. While written for a graduate and professional audience, the book does not require advanced statistical knowledge. It is ideal as a supplemental text for a graduate course on experimental design and the analysis of variance, or as the primary source for a seminar on quasi-experimental design and analysis. Practicing scientists will want to own this book to understand how best to confront analytic issues in their empirical research and interpret their results. --Keith F. Widaman, PhD, Distinguished Professor, Graduate School of Education, University of California, Riverside Reichardt provides an expansive treatment of quasi-experimental designs, in the tradition of Shadish, Cook, and Campbell. This book includes up-to-date discussions of propensity scores, modern missing data procedures, and instrumental variables. Students will appreciate the numerous examples that help clarify the concepts. I would recommend this book for any graduate research methods class--I will certainly use it myself. --Felix J. Thoemmes, PhD, Department of Human Development and Department of Psychology, Cornell University Ever wonder how to best design a quasi-experimental study? This book will help you figure out which research questions best lend themselves to this type of experimental design. Have you collected data from a quasi-experiment and now want to make sure that you correctly analyze and interpret the results? This book addresses the assumptions that must be met, potential pitfalls, and statistical considerations. As an educational psychologist who teaches students across disciplines, I recommend this book as an up-to-date reference on quasi-experimental designs. Randomized controlled trials are not always feasible, for many reasons, so the way this text is framed is actually more useful for fields like education and the social sciences. --Meagan C. Arrastia-Chisholm, PhD, Department of Psychology, Counseling, and Family Therapy, Valdosta State University

Ever wonder how to best design a quasi-experimental study? This book will help you figure out which research questions best lend themselves to this type of experimental design. Have you collected data from a quasi-experiment and now want to make sure that you correctly analyze and interpret the results? This book addresses the assumptions that must be met, potential pitfalls, and statistical considerations. As an educational psychologist who teaches students across disciplines, I recommend this book as an up-to-date reference on quasi-experimental designs. Randomized controlled trials are not always feasible, for many reasons, so the way this text is framed is actually more useful for fields like education and the social sciences. --Meagan C. Arrastia-Chisholm, PhD, Department of Psychology, Counseling, and Family Therapy, Valdosta State University This book not only compiles a comprehensive list of methods on quasi-causal design, but also problematizes causes of biases even in perfectly executed experimental and quasi-experimental designs. The author's take on the ways in which quasi-experiments could potentially render better results than randomized experiments is refreshing and important. Professors will want to discuss this book in their classes. I highly recommend it for students and even more experienced researchers--the author highlights the fundamentals of each approach along with its strengths and limitations. --Manuel Gonz lez Canch , PhD, Higher Education, Quantitative Methods, and Education Policy Divisions, Graduate School of Education, University of Pennsylvania Reichardt provides an up-to-date, expansive treatment of quasi-experimental designs, in the tradition of Shadish, Cook, and Campbell. This book includes lengthier discussions of propensity scores, modern missing data procedures, and instrumental variables. Students will appreciate the numerous examples that help clarify the concepts. I would recommend this book for any graduate research methods class--I will certainly use it myself. --Felix J. Thoemmes, PhD, Department of Human Development and Department of Psychology, Cornell University A 'must read.' After a thorough presentation of strengths of randomized experiments, Reichardt provides an remarkably up-to-date review and synthesis of current thinking on the best, most useful alternatives. Notably, he uses simple and direct language to explain key concepts of the 'counterfactual outcomes' approach for estimating causal effects. While written for a graduate and professional audience, the book does not require advanced statistical knowledge. It is ideal as a supplemental text for a graduate course on experimental design and the analysis of variance, or as the primary source for a seminar on quasi-experimental design and analysis. Practicing scientists will want to own this book to understand how best to confront analytic issues in their empirical research and interpret their results. --Keith F. Widaman, PhD, Distinguished Professor, Graduate School of Education, University of California, Riverside This book represents an important contribution to the literature on research designs that may be implemented when randomized experiments are not feasible or are limited. Covering the full range of design alternatives, this is the first text that fuses important recent advances from statistics and econometrics into Campbell's pioneering approach. A notable feature is Reichardt's careful attention to issues that arise in each research design; he offers innovative design and analysis strategies that can minimize these issues and permit the strongest possible conclusions from research. Clearly written, this text is an outstanding choice for courses focusing on key issues of research design, and is suitable for graduate students with only a basic background in statistics. Established researchers will find it to be a valuable reference that offers new insights for strengthening research designs so that they yield the most credible possible evidence. --Stephen G. West, PhD, Department of Psychology, Arizona State University

This book represents an important contribution to the literature on research designs that may be implemented when randomized experiments are not feasible or are limited. Covering the full range of design alternatives, this is the first text that fuses important recent advances from statistics and econometrics into Campbell's pioneering approach. A notable feature is Reichardt's careful attention to issues that arise in each research design; he offers innovative design and analysis strategies that can minimize these issues and permit the strongest possible conclusions from research. Clearly written, this text is an outstanding choice for courses focusing on key issues of research design, and is suitable for graduate students with only a basic background in statistics. Established researchers will find it to be a valuable reference that offers new insights for strengthening research designs so that they yield the most credible possible evidence. --Stephen G. West, PhD, Department of Psychology, Arizona State University This book not only compiles a comprehensive list of methods on quasi-causal design, but also problematizes causes of biases even in perfectly executed experimental and quasi-experimental designs. The author's take on the ways in which quasi-experiments could potentially render better results than randomized experiments is refreshing and important. Professors will want to discuss this book in their classes. I highly recommend it for students and even more experienced researchers--the author highlights the fundamentals of each approach along with its strengths and limitations. --Manuel Gonz lez Canch , PhD, Higher Education, Quantitative Methods, and Education Policy Divisions, Graduate School of Education, University of Pennsylvania A 'must read.' After a thorough presentation of the strengths of randomized experiments, Reichardt provides a remarkably up-to-date review and synthesis of current thinking on the best, most useful alternatives. Notably, he uses simple and direct language to explain key concepts of the 'counterfactual outcomes' approach for estimating causal effects. While written for a graduate and professional audience, the book does not require advanced statistical knowledge. It is ideal as a supplemental text for a graduate course on experimental design and the analysis of variance, or as the primary source for a seminar on quasi-experimental design and analysis. Practicing scientists will want to own this book to understand how best to confront analytic issues in their empirical research and interpret their results. --Keith F. Widaman, PhD, Distinguished Professor, Graduate School of Education, University of California, Riverside Reichardt provides an expansive treatment of quasi-experimental designs, in the tradition of Shadish, Cook, and Campbell. This book includes up-to-date discussions of propensity scores, modern missing data procedures, and instrumental variables. Students will appreciate the numerous examples that help clarify the concepts. I would recommend this book for any graduate research methods class--I will certainly use it myself. --Felix J. Thoemmes, PhD, Department of Human Development and Department of Psychology, Cornell University Ever wonder how to best design a quasi-experimental study? This book will help you figure out which research questions best lend themselves to this type of experimental design. Have you collected data from a quasi-experiment and now want to make sure that you correctly analyze and interpret the results? This book addresses the assumptions that must be met, potential pitfalls, and statistical considerations. As an educational psychologist who teaches students across disciplines, I recommend this book as an up-to-date reference on quasi-experimental designs. Randomized controlled trials are not always feasible, for many reasons, so the way this text is framed is actually more useful for fields like education and the social sciences. --Meagan C. Arrastia-Chisholm, PhD, Department of Psychology, Counseling, and Family Therapy, Valdosta State University

This book represents an important contribution to the literature on research designs that may be implemented when randomized experiments are not feasible or are limited. Covering the full range of design alternatives, this is the first text that fuses important recent advances from statistics and econometrics into Campbell's pioneering approach. A notable feature is Reichardt's careful attention to issues that arise in each research design; he offers innovative design and analysis strategies that can minimize these issues and permit the strongest possible conclusions from research. Clearly written, this text is an outstanding choice for courses focusing on key issues of research design, and is suitable for graduate students with only a basic background in statistics. Established researchers will find it to be a valuable reference that offers new insights for strengthening research designs so that they yield the most credible possible evidence. --Stephen G. West, PhD, Department of Psychology, Arizona State University This book not only compiles a comprehensive list of methods on quasi-causal design, but also problematizes causes of biases even in perfectly executed experimental and quasi-experimental designs. The author's take on the ways in which quasi-experiments could potentially render better results than randomized experiments is refreshing and important. Professors will want to discuss this book in their classes. I highly recommend it for students and even more experienced researchers--the author highlights the fundamentals of each approach along with its strengths and limitations. --Manuel Gonz lez Canch , PhD, Higher Education, Quantitative Methods, and Education Policy Divisions, Graduate School of Education, University of Pennsylvania A 'must read.' After a thorough presentation of strengths of randomized experiments, Reichardt provides an remarkably up-to-date review and synthesis of current thinking on the best, most useful alternatives. Notably, he uses simple and direct language to explain key concepts of the 'counterfactual outcomes' approach for estimating causal effects. While written for a graduate and professional audience, the book does not require advanced statistical knowledge. It is ideal as a supplemental text for a graduate course on experimental design and the analysis of variance, or as the primary source for a seminar on quasi-experimental design and analysis. Practicing scientists will want to own this book to understand how best to confront analytic issues in their empirical research and interpret their results. --Keith F. Widaman, PhD, Distinguished Professor, Graduate School of Education, University of California, Riverside Reichardt provides an expansive treatment of quasi-experimental designs, in the tradition of Shadish, Cook, and Campbell. This book includes up-to-date discussions of propensity scores, modern missing data procedures, and instrumental variables. Students will appreciate the numerous examples that help clarify the concepts. I would recommend this book for any graduate research methods class--I will certainly use it myself. --Felix J. Thoemmes, PhD, Department of Human Development and Department of Psychology, Cornell University Ever wonder how to best design a quasi-experimental study? This book will help you figure out which research questions best lend themselves to this type of experimental design. Have you collected data from a quasi-experiment and now want to make sure that you correctly analyze and interpret the results? This book addresses the assumptions that must be met, potential pitfalls, and statistical considerations. As an educational psychologist who teaches students across disciplines, I recommend this book as an up-to-date reference on quasi-experimental designs. Randomized controlled trials are not always feasible, for many reasons, so the way this text is framed is actually more useful for fields like education and the social sciences. --Meagan C. Arrastia-Chisholm, PhD, Department of Psychology, Counseling, and Family Therapy, Valdosta State University

Author Information

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.

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