Introduces the concepts and methods of applied behavior analysis based on solid theory and research. Provides strategies for developing and implementing solutions to problem situations. Emphasizes a systems approach as a means of initiating more effective and durable change.

How to Think Like a Behavior Analyst is a revolutionary resource for understanding complex human behavior and making potentially significant quality of life improvements. Behavior analysts offer a worldview of the human condition different than almost any other professional perspective. To a behavior analyst, human behavior is largely learned and subject to change if the right variables are put into play. This is an empowering outlook, providing an opportunity for individuals to analyze the actions of those around them and an understanding of why others exhibit such behavior. Practical, clear, and direct, this book addresses basic questions such as how behavior analysis is different from psychotherapy, what analysis involves, and the meaning of evidence-based treatment. A chapter on Applications presents tips on using behavioral procedures to improve lives and deal with others, and articulates how behavioral procedures are used in community settings. In question and answer format, the text thoroughly covers 50 frequently asked questions about behavior analysis in an educational and entertaining manner. It was developed out of questions raised by students in behavior analysis classes over the last 35 years, as well as questions raised by consumers of behavior analysis services. This text is written for all professionals concerned with behavior, including undergraduate students in psychology and behavior analysis, parents, teachers, employers, and employees. The book can easily be used as a supplement to primary texts in introductory psychology courses, and the exercises that follow each question can be used to stimulate lively discussion in role-play and other active learning situations.

Applied Behavior Analysis (ABA) is a highly functional discipline that, instead of searching for abstract, internal causes for human behavior, looks to external factors that can be influenced. Once identified, these factors can be manipulated to make meaningful, positive improvements in the lives of real people through positive behavior change. Not surprisingly behavior analysis has been applied to a wide range of human activities, from helping troubled teens to organizing industry to maximizing sports performance. ABA interventions for these diverse problems are often creative-and they tend to be effective. In this volume, some of the field's foremost practitioners offer their expert perspective on a range of topics within ABA. Each chapter is fully referenced and contains a set of reading objectives to facilitate deeper understanding and further discussion of its subject area. While these discussions will be of particular interest to academic behavior analysts and graduate students, clinicians and other practitioners will find the research review helpful and informative.

This book describes various ABA terms with examples and illustrations. It is good for students who study ABA, people who are pursuing BCBA(R), BCaBA(R) or RBT(R), people who use ABA for their profession, parents who are raising children with Autism and other related disorders and anyone who is interested in learning ABA.

Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in electronic records. The included examples demonstrate how to perform common data analysis tasks such as: data exploration, visualization, preprocessing, data representation, model training and evaluation. All of this, using the R programming language and real-life behavioral data. Even though the examples focus on behavior analysis tasks, the covered underlying concepts and methods can be applied in any other domain. No prior knowledge in machine learning is assumed. Basic experience with R and basic knowledge in statistics and high school level mathematics are beneficial. Features: Build supervised machine learning models to predict indoor locations based on WiFi signals, recognize physical activities from smartphone sensors and 3D skeleton data, detect hand gestures from accelerometer signals, and so on. Program your own ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources. Use unsupervised learning algorithms to discover criminal behavioral patterns. Build deep learning neural networks with TensorFlow and Keras to classify muscle activity from electromyography signals and Convolutional Neural Networks to detect smiles in images. Evaluate the performance of your models in traditional and multi-user settings. Build anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish behaviors. This book is intended for undergraduate/graduate students and researchers from ubiquitous computing, behavioral ecology, psychology, e-health, and other disciplines who want to learn the basics of machine learning and deep learning and for the more experienced individuals who want to apply machine learning to analyze behavioral data.

This very practical, how-to text provides the beginning researcher with the basics of applied behavior analysis research methods. In 10 logical steps, this text covers all of the elements of single-subject research design and it provides practical information for designing, implementing, and evaluating studies. Using a pocketbook format, the authors provide novice researcher with a "steps-for-success" approach that is brief, to-the-point, and clearly delineated.