Single-case and Small-n Experimental Designs: A Practical Guide To Randomization Tests

This book is a practical guide to help researchers draw valid causal inferences from small-scale clinical intervention studies. It should be of interest to teachers of, and students in, courses with an experimental clinical component, as well as clinical researchers.

Inferential statistics used in the analysis of group data are frequently invalid for use with data from single-case experimental designs. Even non-parametric rank tests provide, at best, approximate solutions for only some single-case (and small-n ) designs. Randomization (Exact) tests, on the other hand, can provide valid statistical analyses for all designs that incorporate a random procedure for assigning treatments to subjects or observation periods, including single-case designs. These Randomization tests require large numbers of data rearrangements and have been seldom used, partly because desktop computers have only recently become powerful enough to complete the analyses in a reasonable time. Now that the necessary computational power is available, they continue to be under-used because they receive scant attention in standard statistical texts for behavioral researchers and because available programs for running the analyses are relatively inaccessible to researchers with limited statistical or computing interest.

This book is first and foremost a practical guide, although it also presents the theoretical basis for Randomization tests. Its most important aim is to make these tests accessible to researchers for a wide range of designs. It does this by providing programs on CD-ROM that allow users to run analyses of their data within a standard package (Minitab, Excel, or SPSS) with which they are already familiar. No statistical or computing expertise is required to use these programs. This is the "new stats" for single-case and small-n intervention studies, and anyone interested in this research approach will benefit.

"This new edition provides an excellent treatment of both the design and the analysis of single-case and small-n designs. It emphasizes the importance of matching the design to the analysis, and uses the many strengths of randomization tests to overcome problems with standard parametric procedures applied to small-sample studies." - David C. Howell, University of Vermont, USA

"This book provides statistical methods appropriate for small n studies--studies that may be messy, exploratory, and fail many of the assumptions of classical methods. A must-read for researchers conducting field research in educational and training environments." - Gregory K.W.K. Chung, UCLA/CRESST, USA

"Although we have known for many years that single case experimental designs are essential for the evaluation of an individual’s response to treatment, most of us do not employ randomization strategies when planning this treatment. We need to change and this book will enable us to do just that. I urge all clinical and neuro psychologists interested in treating patients to purchase this book."  - Barbara A Wilson, Oliver Zangwill Centre, Ely, UK

"I’m very excited about this book. ... The authors ... bring up the issues that I’ve found [students] to struggle with. ... This text will align well with NIH’s and NIMH’s move towards translational research and focus on evidenced-based treatment validity. ...The authors have an incredibly clear, thoughtful writing style. ... This text will "bridge the gap" between required course content and the reality that students will face in the field. ... I plan to buy it, use it in my class, and tell everyone I can about it." - Marie S. Hammond, Tennessee State University, USA

"The text ... fills a gap in the scholarly literature desperately needed in the behavior analytic scientific community. ... [There] are no directly competing texts that go into such depth ... for single-subject research designs as they are used specifically within clinical psychology and behavior analysis. ... [It is] an invaluable ... reference." – Michele Ennis Soreth, Rowan University, USA