Single-Case and Small-N Experimental Designs: A Practical Guide to Randomization Tests

Single-Case and Small-N Experimental Designs: A Practical Guide to Randomization Tests

Single-Case and Small-N Experimental Designs: A Practical Guide to Randomization Tests

Single-Case and Small-N Experimental Designs: A Practical Guide to Randomization Tests

Synopsis

This practical guide explains the use of randomization tests and provides example designs and macros for implementation in IBM SPSS and Excel. It reviews the theory and practice of single-case and small-ndesigns so readers can draw valid causal inferences from small-scale clinical studies. The macros and example data are provided on the book's website so that users can run analyses of the text data as well as data from their own studies.

The new edition features:

  • More explanation as to why randomization tests are useful and how to apply them.
  • More varied and expanded examples that demonstrate the use of these tests in education, clinical work and psychology.
  • A website with the macros and datasets for all of the text examples in IBM SPSS and Excel.
  • Exercises at the end of most chapters that help readers test their understanding of the material.
  • A new glossary that defines the key words that appear in italics when they are first introduced.
  • A new appendix that reviews the basic skills needed to do randomization tests.
  • New appendices that provide annotated SPSS and Excel macros to help readers write their own or tinker with the ones provided in the book.

The book opens with an overview of single case and small ndesigns -- why they are needed and how they differ from descriptive case studies. Chapter 2 focuses on the basic concepts of randoization tests. Next how to choose and implement a randomization design is reviewed including material on how to perform the randomizations, how to select the number of observations, and how to record the data. Chapter 5 focuses on how to analyze the data including how to use the macros and understand the results. Chapter 6 shows how randomization tests fit into the body of statistical inference. Chapter 7 discusses size and power. The book concludes with a demonstration of how to edit or modify the macros or use parts of them to write your own.

Ideal as a text for courses on single-case, small n design, and/or randomization tests taught at the graduate level in psychology (especially clinical, counseling, educational, and school), education, human development, nursing, and other social and health sciences, this inexpensive book also serves as a supplement in statistics or research methods courses. Practitioners and researchers with an applied clinical focus also appreciate this book's accessible approach. An introduction to basic statistics, SPSS, and Excel is assumed.

Excerpt

This book is intended as a practical guide for students and researchers who are interested in the statistical analysis of data from single-case or very small-n experiments. We said this in the preface to the first edition, and it is true of this new edition as well. Randomization tests are not a new idea, but they became really useful only after the advent of fast computing. John Todman, lead author of the first edition of this book, wanted to make randomization tests accessible to many more potential users by providing the means to use them within familiar statistical software. As such, this book serves as an introduction to randomization tests and provides macros to perform some of them in the familiar environments of spss and Excel, which are already being used for data analysis by thousands of researchers. Minitab was dropped from this edition, because we believe fewer people are using it now, but the macros (plus a new one) for use with spss and Excel are still a central feature of the book. As well as the macros, we provide all the information you need to apply them to your own data.

Because most researchers are not familiar with randomization designs and tests, even if they are quite confident with data analysis and statistical inference, we have described the basic ideas of randomization tests using several extended examples. We also use examples to show how to choose a suitable design and, having made a choice, how to implement it and analyze the results. the emphasis throughout is on practical application and understanding when and how to use randomization tests. There is a book Web site (http://www.researchmethodsarena.com/9780415886932) from which you can take the macros and example data. There is a separate site for instructors only that contains the solutions to the exercises that are not included in the text.

If you are among that majority of researchers and students who are unfamiliar with randomization tests, studying this book will enable you to understand how randomization tests work, why they can be used in . . .

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