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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.

Sampling techniques for national surveys have constrained the statistical power in estimating prevalence rates of child mental health problems in minority ethnic groups. To establish the prevalence rates of mental health problems in ethnic Indian adolescents in England and compare these with matched White adolescents living in the same areas. A cross-sectional survey with oversampling of Indian adolescents aged 13-15 years of age. The sample size was 2900 (71% response rate) with 1087 (37%) Indian and 414 (14%) White adolescents. Ethnically Indian adolescents had lower rates of all types of mental health problems (5% v. 13% and 21% v. 30% for abnormal Strengths and Difficulties Questionnaire and Short Mood and Feelings Questionnaire scores, respectively) and substance misuse (18% v. 57%, 5% v.15% and 6% v. 9% for regular alcohol, smoking and drug use, respectively), with the exception of eating disorders, compared with their White counterparts. The odds of an abnormal score on the mental health questionnaires were worse for White compared with Indian children irrespective of sociodemographic variables. Factors relating to how Indian adolescents are parented or their social support networks may be influencing their mental health and may warrant further investigation.

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

Purpose Group identification has been shown to be associated with reduced risk of depression, but this research has important limitations. Our aim was to establish a robust link between group identification and depression whilst overcoming previous studies’ shortcomings. Methods 1824 participants, recruited from General Practice throughout Scotland, completed a questionnaire measuring their identification with three groups (family, community, and a group of their choice), as well as their intensity of contact with each group. They also completed a self-rated depression measure and provided demographic information. Their medical records were also accessed to determine if they had been prescribed antidepressants in the previous 6 months. Results The number of group identifications was associated with both lower self-rated depression and lower odds of having received a prescription for antidepressants, even after controlling for the number of contact-intensive groups, level of education, gender, age, and relationship status. Conclusions Identifying with multiple groups may help to protect individuals against depression. This highlights the potential importance of social prescriptions, where health professionals encourage a depressed patient to become a member of one or more groups with which the patient believes he/she would be likely to identify.

A variety of studies have shown that group identification (a sense of belonging to one’s social group, coupled with a sense of commonality with the group’s members) is linked to high levels of satisfaction with life (SWL). The aim of the present study was to support and extend this literature by: (1) investigating the link between group identification and SWL with a large cross-cultural community sample; (2) examining whether the relationship is moderated by nationality; and (3) considering whether SWL is enhanced by possessing multiple group identifications simultaneously. Utilizing data from Wave 1 of the Health in Groups project, 3829 participants from both Scotland and Italy completed a questionnaire assessing their identification with their family, their local community, and a group of their choice, as well as their level of SWL. Higher identification with each group predicted higher SWL. Nationality was a marginal moderator of the relationship between family identification and SWL, with the relationship being stronger for Italian participants than for Scottish participants. There was also an additive effect of group identification, with a positive relationship between the number of groups with which participants identified and their SWL. These effects were obtained even after controlling for gender, age, employment status, nationality, and extent of contact with each group. The implications for healthcare professionals and their patients are discussed.

Background Despite the available epidemiological evidence on the prevalence of mental health problems in childhood and adolescence, there is limited knowledge on whether there are differences in the level of need and service utilisation by young ethnic minority groups. Methods Adolescents of 13–15 years from nine schools in two English cities in which children of Indian ethnicity were over-represented (n=2900), completed rating scales on different types of mental health problems, contacts with services and informal supports. Results Indian adolescents scored significantly lower on general mental health and depression symptoms. They were also less likely than White adolescents to self-report having mental health problems, even for a similar level of need. Among those with mental health scores within the clinical range, Indian adolescents were less likely to have visited specialist services. Instead, they were more likely to first approach family members, teachers or general practitioners. Conclusions Rather than a blanket approach being applied to policy and service planning to meet the needs of diverse communities of young people, more specific evidence needs to be gained about patterns of referrals of minority groups and their strategy of accessing supportive adults.

[...]interpretive differences between Indian and White British adolescents that question the validity of our findings beg a broader question of how dissimilar these ethnic groups actually are in terms of their ability to understand SDQ items. [...]arguing that our findings may be due to the Indian adolescents' lack of understanding of the SDQ is unlikely. All of these factors affect an adolescent's internalised cultural norms and values as part of their ethnic identity, which can then affect their health. 4 One therefore needs to be careful when generalising all migrant groups to high-income countries as possessing the same set of characteristics or similar abilities for integration. 5 Discussions of the origins of mental health differences in immigrant ethnic minority groups are crucial in our need to fully understand the complex processes involved.