Includes bibliographical references and index.

The possibilities mobile sensing opens up for the social, behavioral, biomedical, and life sciences appear almost infinite and are bound to become even more comprehensive in the years to come. However, data collection with new information technology also poses new challenges for research and applied fields. Is everything that is possible also legally allowed? What are the personal and societal consequences of the possible deep insights into very private areas of life for research ethics and the relations between the researchers and those being researched? How can data be stored so that anonymity and privacy are preserved? How can quality criteria be formulated for this new and rapidly developing field of research? And how can we ensure that information and predictions derived from mobile sensing are psychometrically accurate and practically useful as we move from scientific proof-of-concept measurements to medical/clinical measurements that aim at supporting and improving the diagnostic process? This handbook answers these questions and based on the conviction that a profound understanding and the sound application of mobile sensing methods require specific knowledge and competencies: scientific background and the key concepts, how to generally plan and conduct a mobile sensing study, different methods of data collection with mobile sensing, both in terms of the technological know-how and the methodological how-to, and possibilities and limitations of mobile sensing and of best-practice examples from different areas of application-- How can large-scale, real-time, and real-world data on people's behaviors, interactions, and environments improve psychological measurement, or lead to customized psychological interventions? Written expressly for social and behavioral scientists, this cutting-edge handbook describes the key concepts and tools of mobile sensing and explains how to plan and conduct a mobile sensing study. Renowned experts address the whats, whys, and how-tos of collecting "big data" using smartphones and other wearables, and explore which research questions can best be addressed with these tools. Modern statistical methods for analyzing mobile sensing data are described; for example, dynamic structural equation modeling, network modeling, and machine learning, including deep neural networks. The book includes best-practice research examples of applications in clinical psychology, aging, neuroscience, health, emotions, relationships, personality, the workplace, and other areas. Key methodological challenges and ethical/privacy issues are highlighted throughout. --