Digital Footprints of Sensation Seeking

Schoedel, Ramona; Au, Quay; Völkel, Sarah Theres; Lehmann, Florian; Becker, Daniela; Bühner, Markus; Bischl, Bernd; Hußmann, Heinrich; Stachl, Clemens

doi:10.1027/2151-2604/a000342

Cited by 39 publications

(50 citation statements)

References 56 publications

(107 reference statements)

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“…Furthermore, recent publications do not only examine substantial questions by using ESM (e.g., Rogers & Biesanz, 2018;Sun & Vazire, 2019), but also investigate psychometric properties of scores obtained during ESM (see Horstmann & Ziegler, submitted, for an overview) or combine data from several studies to investigate the role of methodological factors (e.g., Horstmann & Rauthmann, in preparation;Podsakoff, Spoelma, Chawla, & Gabriel, 2019). Additionally, ESM studies that rely on the use of participants' smartphones can simultaneously take advantage of the smartphones' sensors to collect additional informative data (Buschek et al, 2018;Chittaranjan, Blom, & Gatica-Perez, 2013;Harari et al, 2019;Schoedel et al, 2018;Stachl et al, 2017), thereby further thereby further strengthening the usefulness and value of ESM to investigate psychological processes. It is apparent, at this stage, that the ability to understand and conduct ESM studies will become more and more important in psychological research.…”

Section: Background Of Esm Studiesmentioning

confidence: 99%

Experience Sampling and Daily Diary Studies: Basic Concepts, Designs, and Challenges.pdf

Horstmann¹

2020

Preprint

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Experience sampling and daily diary methods have become increasingly popular among psychologists. The repeated assessment of persons in their daily lives allows capturing how a person feels, thinks, or behaves or what he or she desires in the very moment. The current chapter describes basic concepts of experience sampling studies, gives an overview of possible designs and challenges that may be encountered when setting up the first experience sampling study. To overcome these challenges, three basic questions can be answered: (A) What is the construct being measured? (B) What is the purpose of the measure? (C) What is the targeted population of persons and situations? Finally, practical advice is given on how to think through and pilot test an experience sampling study before data collection begins.

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Section: Background Of Esm Studiesmentioning

confidence: 99%

Experience Sampling and Daily Diary Studies: Basic Concepts, Designs, and Challenges.pdf

Horstmann¹

2020

Preprint

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“…In light of the lack of evidence in earlier works, this link clearly needs to be further investigated. For reasons of completeness, we also like to point out a new piece of interesting work investigating the link between the personality trait sensation seeking and tracked smartphone variables [50]. Further work from Denmark presents additional data linking extraversion to several tracked smartphone variables [51].…”

Section: First Validation Data From Personality Psychologymentioning

confidence: 99%

Concept, Possibilities and Pilot-Testing of a New Smartphone Application for the Social and Life Sciences to Study Human Behavior Including Validation Data from Personality Psychology

Montag¹,

Baumeister²,

Kannen

et al. 2019

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With the advent of the World Wide Web, the smartphone and the Internet of Things, not only society but also the sciences are rapidly changing. In particular, the social sciences can profit from these digital developments, because now scientists have the power to study real-life human behavior via smartphones and other devices connected to the Internet of Things on a large-scale level. Although this sounds easy, scientists often face the problem that no practicable solution exists to participate in such a new scientific movement, due to a lack of an interdisciplinary network. If so, the development time of a new product, such as a smartphone application to get insights into human behavior takes an enormous amount of time and resources. Given this problem, the present work presents an easy way to use a smartphone application, which can be applied by social scientists to study a large range of scientific questions. The application provides measurements of variables via tracking smartphone–use patterns, such as call behavior, application use (e.g., social media), GPS and many others. In addition, the presented Android-based smartphone application, called Insights, can also be used to administer self-report questionnaires for conducting experience sampling and to search for co-variations between smartphone usage/smartphone data and self-report data. Of importance, the present work gives a detailed overview on how to conduct a study using an application such as Insights, starting from designing the study, installing the application to analyzing the data. In the present work, server requirements and privacy issues are also discussed. Furthermore, first validation data from personality psychology are presented. Such validation data are important in establishing trust in the applied technology to track behavior. In sum, the aim of the present work is (i) to provide interested scientists a short overview on how to conduct a study with smartphone app tracking technology, (ii) to present the features of the designed smartphone application and (iii) to demonstrate its validity with a proof of concept study, hence correlating smartphone usage with personality measures.

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“…First, ML models have been used to predict individuals' Big Five personality traits from a wide range of data sources; these sources include digital footprints from social media platforms (e.g., Facebook Likes, status updates, Kosinski, Stillwell, & Graepel, 2013;Youyou, Kosinski, & Stillwell, 2015), language samples (Park et al, 2015;Schwartz et al, 2013), spending records (Gladstone, Matz, & Lemaire, 2019), music preferences (Nave et al, 2018), and mobile sensing data (Chittaranjan, Blom, & Gatica-Perez, 2013;De Montjoye, Quoidbach, Robic, & Pentland, 2013;Hoppe, Loetscher, Morey, & Bulling, 2018;Mønsted, Mollgaard, & Mathiesen, 2018;Schoedel et al, 2018;Stachl et al, 2019;W. Wang et al, 2018).…”

Section: Machine Learning In Personality Psychologymentioning

confidence: 99%

“…In addition to the three domains of research noted above, a number of patterns can be discerned in the literature. One pattern concerns differences in the methods used by different disciplines; most psychological studies have used regularized linear regression models (e.g., LASSO) in their analyses (Eisenberg et al, 2019;Kosinski et al, 2013;Park et al, 2015;Schoedel et al, 2018;Schwartz et al, 2013;Youyou et al, 2015) but research in computer science has tended to use more flexible, non-linear algorithms (Chittaranjan et al, 2013;De Montjoye et al, 2013;Mønsted et al, 2018;W. Wang et al, 2018).…”

Section: Machine Learning In Personality Psychologymentioning

confidence: 99%

Personality Research and Assessment in the Era of Machine Learning

Stachl¹,

Pargent²,

Hilbert³

et al. 2019

Preprint

Self Cite

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The increasing availability of high-dimensional, fine-grained data about human behavior, gathered from mobile sensing studies and in the form of digital footprints, is poised to drastically alter the way personality psychologists perform research and undertake personality assessment. These new kinds and quantities of data raise important questions about how to analyze the data and interpret the results appropriately. Machine learning models are well-suited to these kinds of data, allowing researchers to model highly complex relationships and to evaluate the generalizability and robustness of their results using resampling methods. The correct usage of machine learning models requires specialized methodological training that considers issues specific to this type of modeling. Here, we first provide a brief overview of past studies using machine learning in personality psychology. Second, we illustrate the main challenges that researchers face when building, interpreting, and validating machine learning models. Third, we discuss the evaluation of personality scales, derived using machine learning methods. Fourth, we highlight some key issues that arise from the use of latent variables in the modeling process. We conclude with an outlook on the future role of machine learning models in personality research and assessment.

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Digital Footprints of Sensation Seeking

Cited by 39 publications

References 56 publications

Experience Sampling and Daily Diary Studies: Basic Concepts, Designs, and Challenges.pdf

Experience Sampling and Daily Diary Studies: Basic Concepts, Designs, and Challenges.pdf

Concept, Possibilities and Pilot-Testing of a New Smartphone Application for the Social and Life Sciences to Study Human Behavior Including Validation Data from Personality Psychology

Personality Research and Assessment in the Era of Machine Learning

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