Psycho-Informatics: Big Data shaping modern psychometrics

Alexander Markowetz, Konrad Błaszkiewicz, Christian Montag, Christina Switala, Thomas E. Schlaepfer

For the first time in history, it is possible to study human behavior on great scale and in fine detail simultaneously. Online services and ubiquitous computational devices, such as smartphones and modern cars, record our everyday activity. The resulting Big Data offers unprecedented opportunities for tracking and analyzing behavior. This paper hypothesizes the applicability and impact of Big Data technologies in the context of psychometrics both for research and clinical applications. It first outlines the state of the art, including the severe shortcomings with respect to quality and quantity of the resulting data. It then presents a technological vision, comprised of (i) numerous data sources such as mobile devices and sensors, (ii) a central data store, and (iii) an analytical platform, employing techniques from data mining and machine learning. To further illustrate the dramatic benefits of the proposed methodologies, the paper then outlines two current projects, logging and analyzing smartphone usage. One such study attempts to thereby quantify severity of major depression dynamically; the other investigates (mobile) Internet Addiction. Finally, the paper addresses some of the ethical issues inherent to Big Data technologies. In summary, the proposed approach is about to induce the single biggest methodological shift since the beginning of psychology or psychiatry. The resulting range of applications will dramatically shape the daily routines of researches and medical practitioners alike. Indeed, transferring techniques from computer science to psychiatry and psychology is about to establish Psycho-Informatics, an entire research direction of its own.