Single-case and Small-n Experimental Designs: A Practical Guide To Randomization Tests, Second Edition / Edition 2

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Overview

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.

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Editorial Reviews

From the Publisher
"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

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Product Details

  • ISBN-13: 9780415886222
  • Publisher: Taylor & Francis
  • Publication date: 11/24/2011
  • Edition number: 2
  • Pages: 304
  • Product dimensions: 5.90 (w) x 9.10 (h) x 0.90 (d)

Meet the Author

Pat Dugard taught statistics at the University of Abertay Dundee until 1999 and has also taught courses at the Open University. She now concentrates on writing. She received her PGDip in Mathematical Statistics from the University of Cambridge.

Portia File is a psychologist and computer scientist experienced in teaching university courses on research methods. She taught at University of Abertay Dundee from 1983 until 2007. She received her PhD in Cognitive Psychology from the University of Texas at Austin in 1975.

Jonathan Todman is a Clinical Psychologist in the Pain Management Programme at NHS Greater Glasgow and Clyde in Glasgow, Scotland. He received his Clinical Psychology Doctorate from Edinburgh in 2010.

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Table of Contents

Contents: Preface. Data Analysis in Single-Case and Small-n Experiments. Statistical and Visual Analysis. Approaches to Statistical Inference. Principles of Randomization Tests. Randomization Designs for Single-Case and Small-n Studies. Randomization Tests Using Minitab. Randomization Tests Using Excel. Randomization Tests Using SPSS. Other Sources of Randomization Tests. The Use of Randomization Tests With Nonrandomized Designs. The Power of Randomization Tests. Creating Your Own Randomization Tests.

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