Matthias R. Mehl – författare

Bringing together leading authorities, this unique handbook reviews the breadth of current approaches for studying how people think, feel, and behave in everyday environments, rather than in the laboratory. The volume thoroughly describes experience sampling methods, diary methods, physiological measures, and other self-report and non-self-report tools that allow for repeated, real-time measurement in natural settings. Practical guidance is provided to help the reader design a high-quality study, select and implement appropriate methods, and analyze the resulting data using cutting-edge statistical techniques. Applications across a wide range of psychological subfields and research areas are discussed in detail.

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.

Bringing together leading authorities, this unique handbook reviews the breadth of current approaches for studying how people think, feel, and behave in everyday environments, rather than in the laboratory. The volume thoroughly describes experience sampling methods, diary methods, physiological measures, and other self-report and non-self-report tools that allow for repeated, real-time measurement in natural settings. Practical guidance is provided to help the reader design a high-quality study, select and implement appropriate methods, and analyze the resulting data using cutting-edge statistical techniques. Applications across a wide range of psychological subfields and research areas are discussed in detail.