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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- n designs 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 n designs -- 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. \"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 Preface. 1. Single-case and Small- n Designs in Context. 2. Understanding Randomization Tests. 3. Obtaining the Data: Choosing the Design. 4. Obtaining the Data: Implementing the Design. 5. Analyzing the Data: Using the Macros. 6. Analyzing the Data: Wider Considerations. 7. Size and Power. 8. Going Further. Appendixes: 1. Basic Skills for Randomization Tests. 2. SPSS Macros. 3. Excel Macros. 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.

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-n designs 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 n designs -- 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 part

Estimating the power of randomization tests is not always straightforward. Also, especially for phase designs, low power can be a problem. In this chapter, we consider ways to estimate power and also we review some of the ways in which power can be maximized. These are generally good experimental practice and apply to designs other than the randomization designs we are concerned with here. Phase designs and their special problems are discussed in the final section of the chapter. For these designs, effort spent on maximizing power is particularly important.

In this chapter we consider how to go about choosing a design for the problem we want to investigate. There are advantages and disadvantages to any design, and whatever we are investigating, we want to find a design that will address the question as directly as possible and that will have few disadvantages. We have to consider any factors that limit our choice of design, and if several types of design could be suitable, we need to be sure we know the advantages and disadvantages of each. In this introductory section, we briefly describe the classes of designs. Later sections will give the conditions in which each design may be used.

If the last chapter enabled you to choose a design that suits your investigation, this one will show you exactly how to implement it. This includes details of how to carry out the randomizations. You may need to refer to Appendix 1 if you are not confident with the random number facilities in your chosen analysis package, but we will draw attention to this when necessary. Chapter 5 shows how to analyze the designs using our macros in either SPSS or Excel and gives the results for the examples used here.

This chapter shows you how to use the macros for each of the designs described in the previous two chapters. In case you haven't used macros before, Appendices 2 (SPSS) and 3 (Excel) demonstrate how to run and edit a macro in each of the packages. Also in the appendices, we have provided listings of the macros with comments, but you only need to read these long sections if you want to edit the macros or write new ones yourself. To use the macros as they are, the easiest way is to take them from the book Web site (http://www.researchmethodsarena.com/9780415886932) along with the example data used for illustration in this chapter and the previous one. You will probably want to run the macros with the example data yourself before applying them to your own data. We remind you that when you run the macros with our example data for yourself, although the actual test statistics will be the same, the counts of arrangement statistics at least as extreme are unlikely to be exactly the same as ours because we take a random sample from the reference set, as explained in Chapter 2. The corresponding probabilities also vary a bit from one run to another. The SPSS and Excel results shown below are those from our own runs. Sometimes we remind you they are sample runs and quote results for another run.

This book is intended to help researchers, students, and teachers in clinical, educational, and other areas involving human participants. Research in these areas is often aimed at testing the efficacy of a treatment or intervention in improving some measure of health or well-being. Psychologists seeking to understand human behavior also often test the effect of an intervention, treatment, or other experimental condition. There are well-established procedures for designing experiments where treatments or interventions can be randomly allocated to large numbers of participants who are representative of a well-defined population. We refer to such designs as large-n or large-group designs. Tests such as t tests and analysis of variance (ANOVA), known collectively as parametric tests, generally provide valid analyses of such designs provided some assumptions about the data are approximately met.

This book offers randomization test macros for just a few designs, and you may by now be wondering why there are not more, or indeed why statistical software such as SPSS does not offer them. When we use the usual parametric and nonparametric statistical tests, we are making use of the fact that if the null hypothesis is true, it is possible to calculate the distribution of the test statistic. The distributions of most of the commonly used test statistics were tabulated decades ago. In order to see how probable is the value of the test statistic we obtained from our experiment, we only have to consult a table. In fact, when using software such as SPSS, we do not even need to do that because the probability under the null hypothesis of a value at least as extreme as the one we obtained is given with the test statistic. In contrast, to use a randomization test, we have to find the values in the reference set ourselves. Whether we list and calculate every value in the reference set, or use a sampling method as in our macros, obtaining the reference set for comparison with our experimental value is not a process that can be fully generalized. So each design needs its own macro or other software. If you want to use a design that does not fit one of our macros, you could see if there is other software that will support the analysis of your chosen design. In the \"Other Sources of Software for Randomization Tests\" section of this chapter, we briefly list some sources of software other than our macros. Another option is to modify one of our macros to fit your design. The penultimate section in this chapter explains how you can do this.