To Challenge the Morning Lark and the Night Owl: Using Smartphone Sensing Data to Investigate Day–Night Behaviour Patterns

Schoedel, Ramona; Pargent, Florian; Au, Quay; Völkel, Sarah Theres; Schuwerk, Tobias; Bühner, Markus; Stachl, Clemens

doi:10.1002/per.2258

Cited by 37 publications

(64 citation statements)

References 87 publications

(250 reference statements)

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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 and status updates, Kosinski et al, 2013; Youyou et al, 2015), language samples (Park et al, 2015; Schwartz et al, 2013), spending records (Gladstone et al, 2019), music preferences (Nave et al, 2018), and mobile sensing data (Chittaranjan et al, 2013; De Montjoye et al, 2013; Hoppe et al, 2018; Mønsted et al, 2018; Schoedel et al, 2018; Stachl et al, 2019; W. Wang et al, 2018). More recently, researchers have started to apply unsupervised ML methods to identify other psychological constructs in digital data (Eichstaedt et al, 2018; Eisenberg et al, 2019; Schoedel et al, 2020).…”

Section: Machine Learning In Personality Psychologymentioning

confidence: 99%

“…Such personalization–based recommender systems have recently gained popularity as a result of the success of the efforts described earlier to predict personality from digital footprints (Settanni et al, 2018; Youyou et al, 2015), text (Park et al, 2015; Schwartz et al, 2013), and mobile sensing data (Stachl et al, 2019). It is valuable to compute users’ personality scores because recommender systems often suffer from a lack of valid constructs on which to base their recommendations.…”

Section: Machine Learning In Personality Psychologymentioning

confidence: 99%

“…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; Settanni et al, 2018; 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%

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Personality Research and Assessment in the Era of Machine Learning

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The increasing availability of high‐dimensional, fine‐grained data about human behaviour, 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 analyse 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 modelling. 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 modelling process. We conclude with an outlook on the future role of machine learning models in personality research and assessment.

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Section: Machine Learning In Personality Psychologymentioning

confidence: 99%

Section: Machine Learning In Personality Psychologymentioning

confidence: 99%

Section: Machine Learning In Personality Psychologymentioning

confidence: 99%

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Personality Research and Assessment in the Era of Machine Learning

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“…A second, more diverse literature stream did not explicitly focus on the prediction of personality from smartphone data but nevertheless reported associations between smartphone data and personality traits (Harari et al, 2019; Montag et al, 2014; Montag et al, 2015; Schoedel et al, 2020; Servia‐Rodriguez et al, 2017; Stachl et al, 2017). One of the earliest large studies investigated usage patterns of WhatsApp and other mobile applications and found that extraversion was positively associated with the duration of daily WhatsApp use, while conscientiousness was negatively related (Montag et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

How Are Personality States Associated with Smartphone Data?

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Smartphones promise great potential for personality science to study people's everyday life behaviours. Even though personality psychologists have become increasingly interested in the study of personality states, associations between smartphone data and personality states have not yet been investigated. This study provides a first step towards understanding how smartphones may be used for behavioural assessment of personality states. We explored the relationships between Big Five personality states and data from smartphone sensors and usage logs. On the basis of the existing literature, we first compiled a set of behavioural and situational indicators, which are potentially related to personality states. We then applied them on an experience sampling data set containing 5748 personality state responses that are self‐assessments of 30 minutes timeframes and corresponding smartphone data. We used machine learning analyses to investigate the predictability of personality states from the set of indicators. The results showed that only for extraversion, smartphone data (specifically, ambient noise level) were informative beyond what could be predicted based on time and day of the week alone. The results point to continuing challenges in realizing the potential of smartphone data for psychological research. © 2020 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psychology

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“…conversations, calling, texting, and app use; Harari et al, 2020; Montag et al, 2015; Stachl et al, 2017), everyday activities (e.g. sleeping and using one's phone; Schoedel et al, 2020 this issue), and situations (e.g. places visited and ambience; Santani et al, 2016).…”

Section: An Agenda For Personality Sensing Researchmentioning

confidence: 99%

Personality Sensing for Theory Development and Assessment in the Digital Age

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People around the world own digital media devices that mediate and are in close proximity to their daily behaviours and situational contexts. These devices can be harnessed as sensing technologies to collect information from sensor and metadata logs that provide fine‐grained records of everyday personality expression. In this paper, we present a conceptual framework and empirical illustration for personality sensing research, which leverages sensing technologies for personality theory development and assessment. To further empirical knowledge about the degree to which personality‐relevant information is revealed via such data, we outline an agenda for three research domains that focus on the description, explanation, and prediction of personality. To illustrate the value of the personality sensing research agenda, we present findings from a large smartphone‐based sensing study (N = 633) characterizing individual differences in sensed behavioural patterns (physical activity, social behaviour, and smartphone use) and mapping sensed behaviours to the Big Five dimensions. For example, the findings show associations between behavioural tendencies and personality traits and daily behaviours and personality states. We conclude with a discussion of best practices and provide our outlook on how personality sensing will transform our understanding of personality and the way we conduct assessment in the years to come. © 2020 European Association of Personality Psychology

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To Challenge the Morning Lark and the Night Owl: Using Smartphone Sensing Data to Investigate Day–Night Behaviour Patterns

Cited by 37 publications

References 87 publications

Personality Research and Assessment in the Era of Machine Learning

Personality Research and Assessment in the Era of Machine Learning

How Are Personality States Associated with Smartphone Data?

Personality Sensing for Theory Development and Assessment in the Digital Age

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