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. Reichardt compares quasi-experiments to randomized experiments and discusses when and why the former might be a better choice. Modern moethods 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.

This issue considers social experiments in practice and how recent advances improve their value and potential applications. Although controversial, it is clear they are here to stay and are in fact increasing. With their greater abundance, experimental evaluations have stretched to address more diverse policy questions, no longer simply providing a treatment–control contrast but adding multiarm, multistage, and multidimensional (factorial) designs and analytic extensions to expose more about what works best for whom. Social experiments are also putting programs under the microscope when they are most ready for testing, enhancing the policy value of their findings. This volume provides new developments in all these areas from scholars instrumental to recent scientific advances. In some instances, established ideas are given new attention, connecting them to new opportunities to learn and inform policy. By all means, this issue aims to encourage stronger and more informative social experiments in the future. This is the 152nd issue in the New Directions for Evaluation series from Jossey-Bass. It is an official publication of the American Evaluation Association.

The contributors to Best Practices in Quantitative Methods envision quantitative methods in the 21st century, identify the best practices, and, where possible, demonstrate the superiority of their recommendations empirically. Editor Jason W. Osborne designed this book with the goal of providing readers with the most effective, evidence-based, modern quantitative methods and quantitative data analysis across the social and behavioral sciences. The text is divided into five main sections covering select best practices in Measurement, Research Design, Basics of Data Analysis, Quantitative Methods, and Advanced Quantitative Methods. Each chapter contains a current and expansive review of the literature, a case for best practices in terms of method, outcomes, inferences, etc., and broad-ranging examples along with any empirical evidence to show why certain techniques are better. Key Features: Describes important implicit knowledge to readers: The chapters in this volume explain the important details of seemingly mundane aspects of quantitative research, making them accessible to readers and demonstrating why it is important to pay attention to these details. Compares and contrasts analytic techniques: The book examines instances where there are multiple options for doing things, and make recommendations as to what is the "best" choice—or choices, as what is best often depends on the circumstances. Offers new procedures to update and explicate traditional techniques: The featured scholars present and explain new options for data analysis, discussing the advantages and disadvantages of the new procedures in depth, describing how to perform them, and demonstrating their use. Intended Audience: Representing the vanguard of research methods for the 21st century, this book is an invaluable resource for graduate students and researchers who want a comprehensive, authoritative resource for practical and sound advice from leading experts in quantitative methods.

Social Experimentation: A Method for Planning and Evaluating Social Intervention summarizes the available knowledge about how randomized experiments might be used in planning and evaluating ameliorative social programs. The book presents various aspects of social experimentation - design, measurement, execution, sponsorship, and utilization of results. Chapters are devoted to topics on experimentation as a method of program planning and evaluation; experimental design and analysis; institutional and political factors in social experimentation; and aspects of time and institutional capacity. Sociologists will find the book a valuable piece of reference.

The concepts of cause and effect are critical to the field of program evaluation. Experimentally-designed evaluations—those that randomize to treatment and control groups—offer a convincing means for establishing a causal connection between a program and its effects. Experimental Evaluation Design for Program Improvement considers a range of impact evaluation questions, particularly those questions that focus on the impact of specific aspects of a program. Laura R. Peck shows how a variety of experimental evaluation design options can provide answers to these questions, and she suggests opportunities for experiments to be applied in more varied settings and focused on program improvement efforts.

We shall examine the validity of 16 experimental designs against 12 common threats to valid inference. By experiment we refer to that portion of research in which variables are manipulated and their effects upon other variables observed. It is well to distinguish the particular role of this chapter. It is not a chapter on experimental design in the Fisher (1925, 1935) tradition, in which an experimenter having complete mastery can schedule treatments and measurements for optimal statistical efficiency, with complexity of design emerging only from that goal of efficiency. Insofar as the designs discussed in the present chapter become complex, it is because of the intransigency of the environment: because, that is, of the experimenter’s lack of complete control.

Sections include: experiments and generalised causal inference; statistical conclusion validity and internal validity; construct validity and external validity; quasi-experimental designs that either lack a control group or lack pretest observations on the outcome; quasi-experimental designs that use both control groups and pretests; quasi-experiments: interrupted time-series designs; regresssion discontinuity designs; randomised experiments: rationale, designs, and conditions conducive to doing them; practical problems 1: ethics, participation recruitment and random assignment; practical problems 2: treatment implementation and attrition; generalised causal inference: a grounded theory; generalised causal inference: methods for single studies; generalised causal inference: methods for multiple studies; a critical assessment of our assumptions.