Description

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.

Mobile Sensing in Psychology: Methods and Applications

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£105.00

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Hardback by Matthias R. Mehl , Michael Eid

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How can large-scale, real-time, and real-world data on people’s behaviors, interactions, and environments improve psychological measurement, or lead to customized... Read more

    Publisher: Guilford Publications
    Publication Date: 01/01/2024
    ISBN13: 9781462553105, 978-1462553105
    ISBN10: 1462553109

    Number of Pages: 802

    Non Fiction , Politics, Philosophy & Society

    Description

    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.

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