This book will be written primarily for graduate students, advanced undergraduates, and professionals in the fields of school psychology, special education, and other areas of education, as well as the health professions. We see the book as being a viable textbook for courses in research design, applied statistics, applied behavioral analysis, and practicum, among others. We would not assume of the readers any prior knowledge about single subjects designs, nor any prior statistical experience. We will provide an introductory chapter devoted to basic statistical concepts, including measures of central tendency (e.g., mean, median, mode), measures of variation (e.g., variance, standard deviation, range, inter-quartile range), correlation, frequency distributions, and effect sizes. In addition, given that the book will rely heavily on R software, the introductory chapter will also devote attention to the basics of using the software for organizing data, conducting basic statistical analyses, and for graphics. The R commands used to carry out these analyses will be largely automated so that users will only need to define the range for their data, and then enter it into the R spreadsheet. We envision these tools being available on the book website, with instructions for using them available in the book itself. We envision the book as being useful either as a primary text for a course in educational research designs, school psychology practicum, applied behavioral analysis, special education, or applied statistics. We also anticipate that individuals working in schools, school districts, mental health facilities, hospitals, applied behavioral analysis clinics, and evaluation organizations, as well as faculty members needing a practical resource for single subject design research, will all serve as a market for the book. In short, the readership would include graduate students, faculty members, teachers, psychologists, social workers, counselors, medical professionals, applied behavioral analysis professionals, program evaluators, and others whose work focuses on monitoring changes in individuals, particularly as the result of specific treatment conditions. We believe that this book could be marketed through professional organizations such as the American Educational Research Association (AERA), the National Association of School Psychologists, the National Association of Special Education Teachers, the Association for Professional Behavior Analysis, the American Psychological Association (APA), the Association for Psychological Science, and the American Evaluation Association. Within AERA, the following special interest groups would have particular interest in this book: Action Research, Classroom Observation, Disability Studies in Education, Mixed Methods Research, Qualitative Research, and Special Education Research. The book could also be marketed to state departments of education and their special education and school psychology divisions. Currently, many state departments of education require documentation for Response to Intervention (RtI) and Multi-Tiered Systems of Support (MTSS) procedures for individual students. The method taught in this proposed book would allow educators and student support personnel to document the effectiveness of interventions systematically and accurately.

The general theme of this book is to present innovative psychometric modeling and methods. In particular, this book includes research and successful examples of modeling techniques for new data sources from digital assessments, such as eye-tracking data, hint uses, and process data from game-based assessments. In addition, innovative psychometric modeling approaches, such as graphical models, item tree models, network analysis, and cognitive diagnostic models, are included. Chapters 1, 2, 4 and 6 are about psychometric models and methods for learning analytics. The first two chapters focus on advanced cognitive diagnostic models for tracking learning and the improvement of attribute classification accuracy. Chapter 4 demonstrates the use of network analysis for learning analytics. Chapter 6 introduces the conjunctive root causes model for the understanding of prerequisite skills in learning. Chapters 3, 5, 8, 9 are about innovative psychometric techniques to model process data. Specifically, Chapters 3 and 5 illustrate the usage of generalized linear mixed effect models and item tree models to analyze eye-tracking data. Chapter 8 discusses the modeling approach of hint uses and response accuracy in learning environment. Chapter 9 demonstrates the identification of observable outcomes in the game-based assessments. Chapters 7 and 10 introduce innovative latent variable modeling approaches, including the graphical and generalized linear model approach and the dynamic modeling approach. In summary, the book includes theoretical, methodological, and applied research and practices that serve as the foundation for future development. These chapters provide illustrations of efforts to model and analyze multiple data sources from digital assessments. When computer-based assessments are emerging and evolving, it is important that researchers can expand and improve the methods for modeling and analyzing new data sources. This book provides a useful resource to researchers who are interested in the development of psychometric methods to solve issues in this digital assessment age.

Psychosocial and holistic approaches to assessment have become a central feature of modern mental health care. This practical and comprehensive book guides students through the theory and practice of psychosocial assessments to help them integrate the data as preparation for the effective planning of treatment and interventions. Key features: step-by-step guide on how to undertake each stage of the assessment process in practice clinical staff and service users voices describing their experiences of the process end of chapter exercises reflections and considerations for practice This is essential reading for pre-registration nursing students and mental health professionals.