Focuses on Donald Campbell's contributions to the concept of validity and the more activist side of his thinking, social experimentation.

Intended to provide a basic understanding not only of how to design and implement social experiments, but also of how to interpret their results once they are completed, author Larry L. Orr's Social Experiments is written in a friendly, how-to manner. Through the use of illustrative examples, how-to exhibits and cases, and boldface key words, Orr provides readers with a grounding in the experimental method, including the rational and ethical issues of random assignment; designs that best address alternative policy questions; maximizing the precision of the estimates; implementing the experiment in the field; data collection; estimating and interpreting program impacts, costs, and benefits; dealing with potential biases; and the use and misuse of experimental results in the policy process. This book will be useful not only to those who plan to conduct experiments, but also to the much larger group who will, at one time or another, want to understand the results of experimental evaluations.

"Contains brief summaries of 240 known completed social experiments. Each summary outlines the cost and time frame of the demonstration, the treatments tested, outcomes of interest, sample sizes and target population, research components, major findings, important methodological limitations and design issues encountered, and other relevant topics. In addition, very brief outlines of 21 experiments and one quasi experiment still in progress [as of April 2003] are also provided"--p. 3.

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.

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.

This book provides researchers, evaluators, and graduate students with a user-friendly presentation of Campbell's essential work (including his thoughts on some of his classic works) in social experimentation.

A thorough and comprehensive guide to the theoretical, practical, and methodological approaches used in survey experiments across disciplines such as political science, health sciences, sociology, economics, psychology, and marketing This book explores and explains the broad range of experimental designs embedded in surveys that use both probability and non-probability samples. It approaches the usage of survey-based experiments with a Total Survey Error (TSE) perspective, which provides insight on the strengths and weaknesses of the techniques used. Experimental Methods in Survey Research: Techniques that Combine Random Sampling with Random Assignment addresses experiments on within-unit coverage, reducing nonresponse, question and questionnaire design, minimizing interview measurement bias, using adaptive design, trend data, vignettes, the analysis of data from survey experiments, and other topics, across social, behavioral, and marketing science domains. Each chapter begins with a description of the experimental method or application and its importance, followed by reference to relevant literature. At least one detailed original experimental case study then follows to illustrate the experimental method’s deployment, implementation, and analysis from a TSE perspective. The chapters conclude with theoretical and practical implications on the usage of the experimental method addressed. In summary, this book: Fills a gap in the current literature by successfully combining the subjects of survey methodology and experimental methodology in an effort to maximize both internal validity and external validity Offers a wide range of types of experimentation in survey research with in-depth attention to their various methodologies and applications Is edited by internationally recognized experts in the field of survey research/methodology and in the usage of survey-based experimentation —featuring contributions from across a variety of disciplines in the social and behavioral sciences Presents advances in the field of survey experiments, as well as relevant references in each chapter for further study Includes more than 20 types of original experiments carried out within probability sample surveys Addresses myriad practical and operational aspects for designing, implementing, and analyzing survey-based experiments by using a Total Survey Error perspective to address the strengths and weaknesses of each experimental technique and method Experimental Methods in Survey Research: Techniques that Combine Random Sampling with Random Assignment is an ideal reference for survey researchers and practitioners in areas such political science, health sciences, sociology, economics, psychology, public policy, data collection, data science, and marketing. It is also a very useful textbook for graduate-level courses on survey experiments and survey methodology.

Policy analysis has grown increasingly reliant on the random assignment experiment—a research method whereby participants are sorted by chance into either a program group that is subject to a government policy or program, or a control group that is not. Because the groups are randomly selected, they do not differ from one another systematically. Therefore any differences between the groups at the end of the study can be attributed solely to the influence of the program or policy. But there are many questions that randomized experiments have not been able to address. What component of a social policy made it successful? Did a given program fail because it was designed poorly or because it suffered from low participation rates? In Learning More from Social Experiments, editor Howard Bloom and a team of innovative social researchers profile advancements in the scientific underpinnings of social policy research that can improve randomized experimental studies. Using evaluations of actual social programs as examples, Learning More from Social Experiments makes the case that many of the limitations of random assignment studies can be overcome by combining data from these studies with statistical methods from other research designs. Carolyn Hill, James Riccio, and Bloom profile a new statistical model that allows researchers to pool data from multiple randomized-experiments in order to determine what characteristics of a program made it successful. Lisa Gennetian, Pamela Morris, Johannes Bos, and Bloom discuss how a statistical estimation procedure can be used with experimental data to single out the effects of a program's intermediate outcomes (e.g., how closely patients in a drug study adhere to the prescribed dosage) on its ultimate outcomes (the health effects of the drug). Sometimes, a social policy has its true effect on communities and not individuals, such as in neighborhood watch programs or public health initiatives. In these cases, researchers must randomly assign treatment to groups or clusters of individuals, but this technique raises different issues than do experiments that randomly assign individuals. Bloom evaluates the properties of cluster randomization, its relevance to different kinds of social programs, and the complications that arise from its use. He pays particular attention to the way in which the movement of individuals into and out of clusters over time complicates the design, execution, and interpretation of a study. Learning More from Social Experiments represents a substantial leap forward in the analysis of social policies. By supplementing theory with applied research examples, this important new book makes the case for enhancing the scope and relevance of social research by combining randomized experiments with non-experimental statistical methods, and it serves as a useful guide for researchers who wish to do so.

Policy Analysis for Social Workers offers a comprehensive, step-by-step guide to understanding the process of policy development and analysis for effective advocacy. This user-friendly model helps students get excited about understanding policy as a product, a process, and as performance—a unique "3-P" approach to policy analysis as competing texts often just focus on one of these areas. Author Richard K Caputo efficiently teaches the purpose of policy and its relation to social work values, discusses the field of policy studies and the various kinds of analysis, and highlights the necessary criteria (effectiveness, efficiency, equity, political feasibility, social acceptability, administrative, and technical feasibility) for evaluating public policy.