- Author : Donald Thomas Campbell
- Publisher : Unknown
- Release Date : 1973
- Genre : Experimental design
- Pages : 84
- ISBN : STANFORD:36105004536699
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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.
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 successor of the original Cook/Campbell Quasi-Experimentation: Design and Analysis Issues for Field Settings represents updates in the field over the last two decades. The book covers four major topics in field experimentation: Theoretical matters: Experimentation, causation, and validity ; Quasi-experimental design: Regression discontinuity designs, interrupted time series designs, quasi-experimental designs that use both pretests and control groups, and other designs ; Randomized experiments: Logic and design issues, and practical problems involving ethics, recruitment, assignment, treatment implementation, and attrition ; Generalized causal inference: A grounded theory of generalized causal inference, along with methods for implementing that theory in single and multiple studies.
This pocket guide describes the logic, design, and conduct of the range of such designs, encompassing pre-experiments, quasi-experiments making use of a control or comparison group, and time-series designs. While it can be utilized as a manual, this book is also valuable for practitioners seeking a greater conceptual understanding of quasi-experimental studies in social work literature. Human service professionals planning to undertake a program evaluation of their agency's services will find this book helpful in understanding the steps and actions needed to adopt a quasi-experimental strategy.
The intent of this volume of New Directions for Program Evaluation is to update, even to alter, our thinking about quasi-experimentation in applied social research and program evaluation. This volume makes the case that we have moved beyond the traditional thinking on quasi-experiments as a collection of specific designs and threats to validity toward a more integrated, synthetic view of quasi-experimentation as part of a general logical epistemological framework for research. This is the 31st issue of New Directions for Program Evaluation. For more information on the series, please see the Journals and Periodicals page.
This title is part of UC Press's Voices Revived program, which commemorates University of California Press's mission to seek out and cultivate the brightest minds and give them voice, reach, and impact. Drawing on a backlist dating to 1893, Voices Revived makes high-quality, peer-reviewed scholarship accessible once again using print-on-demand technology. This title was originally published in 1986.
Marriage as an institution in contemporary culture is in a state of distress, with a risk of divorce rate reaching into the 60% range for couples planning to marry in the near future. Research in the field of marriage and family therapy has attempted to explain this phenomenon, particularly through the study of couple interaction. Validated instruments are available to assess the level of satisfaction and adjustment in a marital relationship. A significant challenge for the marriage clinician is effecting positive change on the level of satisfaction perceived and experienced in the relationship. Other research has demonstrated the effectiveness of prayer as a medium for cognitive change toward optimism and increased health, both emotional and physical. This study explored the effect of specific spousal prayer in the lives of Catholic and Protestant Christian married couples on their level of marital satisfaction by using a nonradomized control group pretest-posttest design.
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.
Practical Statistic for Educators, 6th Edition is a clear and easy-to-follow book written specifically for education students in introductory statistics and action research courses. It is also an invaluable resource and guidebook for educational practitioners who wish to study their own settings and for those involved in program evaluation. The focus of the book is on essential concepts in educational statistics, understanding when to use various statistical tests, and how to interpret results. This book introduces educational students and practitioners to the use of statistics in education and basic concepts in statistics are explained in clear language. All of the examples used to explain the use of statistics in educational research are taken from the field of education and serve to illustrate the various concepts, terms, statistical tests, and data interpretations that are discussed in the book. Formulas and equations are used sparingly and readers are not required to do any computations. The book also includes a discussion of testing, test score interpretation, reliability, and validity. A chapter on survey design and analysis provide the book readers with examples which demonstrate how the different statistical tests introduced in the book can be used to analyzed survey data. Chapter previews are provided, as well as succinct end-of-chapter summaries. The book’s glossary of main terms and concepts helps readers navigate the book and easily find useful information. Review exercises are included at the end of the book to allow readers to practice and apply their newly-acquired knowledge and skills.
Many empirical studies leverage shift-share (or "Bartik") instruments that combine a set of aggregate shocks with measures of shock exposure. We derive a necessary and sufficient shock-level orthogonality condition for these instruments to identify causal effects. We then show that orthogonality holds when observed shocks are as-good-as-randomly assigned and growing in number, with the average shock exposure sufficiently dispersed. We recommend that practitioners implement quasi-experimental shift-share designs with new shock-level regressions, which help visualize identifying shock variation, correct standard errors, choose appropriate specifications, test identifying assumptions, and optimally combine multiple sets of quasi-random shocks. We illustrate these points by revisiting Autor et al. (2013)'s analysis of the labor market effects of Chinese import competition.
"Evidence based practice (EBP) has become the standard in health care practice today. Evidence Based Practice for Health Professionals covers the fundamentals of applying medical evidence to clinical practice and discussing research findings with patients and fellow professionals. This essential text explains the basic concepts of EBP, its applications in health care, and how to interpret biostatistics and biomedical research. With examples derived from multiple health professions, Evidence Based Practice for Health Professionals teaches the skills needed to access and interpret research in order to successfully apply it to collaborative, patient-centered health care decisions. Students gain valuable practice with skill-building learning activities, such as explaining the evidence for treatments to patients, developing a standard of care, selecting a diagnostic tool, and designing community-based educational materials. Evidence Based Practice for Health Professionals also helps prepare students to communicate knowledgeably with members of interprofessional healthcare teams as well as with pharmaceutical sales representatives"--
Using Propensity Scores in Quasi-Experimental Designs, by William M. Holmes, examines how propensity scores can be used to reduce bias with different kinds of quasi-experimental designs and to fix or improve broken experiments. Requiring minimal use of matrix and vector algebra, the book covers the causal assumptions of propensity score estimates and their many uses, linking these uses with analysis appropriate for different designs. Thorough coverage of bias assessment, propensity score estimation, and estimate improvement is provided, along with graphical and statistical methods for this process. Applications are included for analysis of variance and covariance, maximum likelihood and logistic regression, two-stage least squares, generalized linear regression, and general estimation equations. The examples use public data sets that have policy and programmatic relevance across a variety of disciplines.
This book presents some quasi-experimental designs and design features that can be used in many social research settings. The designs serve to probe causal hypotheses about a wide variety of substantive issues in both basic and applied research. Each design is assessed in terms of four types of validity, with special stress on internal validity. Although general conclusions are drawn about the strengths and limitations of each design, emphasis is also placed on the fact that the relevant threats to valid inference are specific to each research setting. Consequently, a threat that is usually associated with a particular design need not invariably be associated with that design.